CDC Daily by State
davegoblue
2/20/2022
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v003, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download
on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220220.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220220.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220220.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220206")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220206")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220206")$dfRaw$vax
)
cdc_daily_220220 <- readRunCDCDaily(thruLabel="Feb 18, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-01-30 new_deaths 796 539 257 0.38501873
## 2 2022-01-29 new_deaths 1394 1098 296 0.23756019
## 3 2022-01-23 new_deaths 868 709 159 0.20164870
## 4 2022-01-22 new_deaths 1176 1028 148 0.13430127
## 5 2022-01-16 new_deaths 807 747 60 0.07722008
## 6 2022-01-25 new_deaths 3445 3220 225 0.06751688
## 7 2022-01-24 new_deaths 2679 2505 174 0.06712963
## 8 2022-01-27 new_deaths 2757 2592 165 0.06169377
## 9 2022-01-17 new_deaths 1429 1350 79 0.05685498
## 10 2022-01-26 new_deaths 3023 2858 165 0.05611291
## 11 2022-01-29 new_cases 195076 173891 21185 0.11483412
## 12 2022-01-30 new_cases 138089 124992 13097 0.09956629
## 13 2022-01-31 new_cases 620416 661083 40667 0.06346786
## 14 2022-02-04 new_cases 272825 289747 16922 0.06015941
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 4003629 3992287 11342 0.002836948
## 2 AL tot_deaths 5972978 5963555 9423 0.001578850
## 3 NC tot_deaths 6808527 6798521 10006 0.001470708
## 4 FL tot_cases 1290393798 1286243847 4149951 0.003221214
## 5 MD tot_cases 232491171 231793719 697452 0.003004414
## 6 KY tot_cases 252489934 252077588 412346 0.001634453
## 7 FL new_deaths 68042 66007 2035 0.030362032
## 8 KY new_deaths 13402 13063 339 0.025618742
## 9 AL new_deaths 17741 17371 370 0.021075416
## 10 NC new_deaths 21278 21097 181 0.008542773
## 11 RI new_deaths 3358 3354 4 0.001191895
## 12 MD new_cases 984492 961805 22687 0.023312989
## 13 KY new_cases 1208554 1193647 14907 0.012411118
## 14 TN new_cases 1912511 1926401 13890 0.007236425
## 15 FL new_cases 5648704 5629602 19102 0.003387388
## 16 NC new_cases 2478266 2470242 8024 0.003242998
## 17 SC new_cases 1408611 1405271 3340 0.002373945
## 18 RI new_cases 348326 347901 425 0.001220866
## 19 PW new_cases 2498 2495 3 0.001201682
##
##
##
## Raw file for cdcDaily:
## Rows: 45,540
## Columns: 15
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-0~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 28~
## $ conf_cases <dbl> 135705, NA, 3685032, NA, NA, 1130917, NA, 176228, NA, 7~
## $ prob_cases <dbl> 27860, NA, 0, NA, NA, 0, NA, 103954, NA, 108997, 0, NA,~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 5~
## $ pnew_case <dbl> 220, 0, 0, 0, 0, 0, 0, 559, 0, 443, 0, 0, 0, 0, NA, 0, ~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408~
## $ conf_death <dbl> NA, 3616, 62011, 9604, NA, 19306, NA, 4739, NA, 10976, ~
## $ prob_death <dbl> NA, 149, 0, 218, NA, 2030, NA, 1991, NA, 1432, NA, 416,~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, ~
## $ pnew_death <dbl> 0, 0, 0, 1, 0, 16, 0, 7, 0, 2, 0, 0, 0, 0, NA, 0, 0, 4,~
## $ created_at <chr> "12/02/2021 02:35:20 PM", "08/19/2020 12:00:00 AM", "06~
## $ consent_cases <chr> "Agree", "N/A", "Agree", "N/A", "Not agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Not agree", "A~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-05 inp 108309 114478 6169 0.05538025
## 2 2022-02-05 hosp_ped 3323 3585 262 0.07585408
## 3 2021-11-24 hosp_ped 1387 1306 81 0.06015596
## 4 2022-02-05 hosp_adult 104794 110893 6099 0.05655417
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NH hosp_ped 725 811 86 0.111979167
## 2 ME hosp_ped 1373 1431 58 0.041369472
## 3 WV hosp_ped 4435 4554 119 0.026476805
## 4 VT hosp_ped 348 357 9 0.025531915
## 5 AR hosp_ped 10602 10393 209 0.019909502
## 6 KS hosp_ped 3856 3929 73 0.018754014
## 7 SC hosp_ped 7275 7393 118 0.016089446
## 8 VI hosp_ped 81 80 1 0.012422360
## 9 MA hosp_ped 9296 9412 116 0.012401112
## 10 ID hosp_ped 3155 3120 35 0.011155378
## 11 KY hosp_ped 15228 15375 147 0.009606901
## 12 NJ hosp_ped 15981 15838 143 0.008988340
## 13 IN hosp_ped 14697 14787 90 0.006105006
## 14 UT hosp_ped 7026 6998 28 0.003993155
## 15 NV hosp_ped 3856 3871 15 0.003882490
## 16 ND hosp_ped 2898 2909 11 0.003788531
## 17 TN hosp_ped 17497 17563 66 0.003764974
## 18 AL hosp_ped 17263 17319 56 0.003238679
## 19 NC hosp_ped 23574 23649 75 0.003176418
## 20 OR hosp_ped 7333 7356 23 0.003131595
## 21 MO hosp_ped 31461 31363 98 0.003119827
## 22 MS hosp_ped 8953 8926 27 0.003020303
## 23 PA hosp_ped 43632 43509 123 0.002823011
## 24 GA hosp_ped 42185 42079 106 0.002515902
## 25 IA hosp_ped 6153 6168 15 0.002434867
## 26 HI hosp_ped 1909 1913 4 0.002093145
## 27 AZ hosp_ped 22800 22847 47 0.002059281
## 28 NE hosp_ped 6181 6170 11 0.001781232
## 29 WA hosp_ped 10469 10484 15 0.001431776
## 30 CO hosp_ped 17474 17499 25 0.001429674
## 31 WI hosp_ped 8578 8568 10 0.001166453
## 32 IL hosp_ped 35034 35073 39 0.001112585
## 33 OK hosp_ped 20546 20524 22 0.001071342
## 34 RI hosp_ped 2843 2846 3 0.001054667
## 35 PR hosp_ped 16962 16979 17 0.001001738
## 36 AK hosp_ped 1996 1998 2 0.001001502
##
##
##
## Raw file for cdcHosp:
## Rows: 38,675
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 27,992
## Columns: 82
## $ date <date> 2022-02-19, 2022-02-19, 2022-0~
## $ MMWR_week <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "~
## $ Distributed <dbl> 20744900, 12186030, 11914970, 1~
## $ Distributed_Janssen <dbl> 916100, 503900, 500200, 926300,~
## $ Distributed_Moderna <dbl> 7813760, 4644240, 4216760, 7835~
## $ Distributed_Pfizer <dbl> 12015040, 7037890, 7198010, 111~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 197795, 178441, 211272, 199329,~
## $ Distributed_Per_100k_12Plus <dbl> 230870, 208823, 249367, 231608,~
## $ Distributed_Per_100k_18Plus <dbl> 253377, 229098, 274762, 253818,~
## $ Distributed_Per_100k_65Plus <dbl> 1184680, 1065780, 1294570, 1127~
## $ vxa <dbl> 16040239, 9551129, 9853584, 150~
## $ Administered_12Plus <dbl> 15576577, 9369683, 9460637, 146~
## $ Administered_18Plus <dbl> 14630091, 8914389, 8826086, 138~
## $ Administered_65Plus <dbl> 4239236, 2778123, 2487485, 4293~
## $ Administered_Janssen <dbl> 508845, 259901, 353693, 459665,~
## $ Administered_Moderna <dbl> 5969173, 3654211, 3581985, 5900~
## $ Administered_Pfizer <dbl> 9561290, 5583600, 5913885, 8724~
## $ Administered_Unk_Manuf <dbl> 931, 53417, 4021, 2106, 133, 32~
## $ Admin_Per_100k <dbl> 152938, 139858, 174720, 151062,~
## $ Admin_Per_100k_12Plus <dbl> 173352, 160562, 198000, 170666,~
## $ Admin_Per_100k_18Plus <dbl> 178691, 167591, 203531, 176626,~
## $ Admin_Per_100k_65Plus <dbl> 242091, 242972, 270267, 243193,~
## $ Recip_Administered <dbl> 15939232, 9383280, 9868373, 153~
## $ Administered_Dose1_Recip <dbl> 8596653, 4180275, 4183752, 6576~
## $ Administered_Dose1_Pop_Pct <dbl> 82.0, 61.2, 74.2, 65.9, 74.7, 0~
## $ Administered_Dose1_Recip_12Plus <dbl> 8331132, 4079234, 3968078, 6351~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 92.7, 69.9, 83.0, 73.9, 86.3, 0~
## $ Administered_Dose1_Recip_18Plus <dbl> 7823417, 3850407, 3680383, 5966~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 72.4, 84.9, 76.1, 88.9, 0~
## $ Administered_Dose1_Recip_65Plus <dbl> 2154949, 1047531, 937204, 16884~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.6, 95.0, 95.0, 95.0, 0~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 0~
## $ Series_Complete_12Plus <dbl> 6011177, 3568185, 3651613, 5701~
## $ Series_Complete_12PlusPop_Pct <dbl> 66.9, 61.1, 76.4, 66.3, 69.0, 0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 0~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 153840~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 0~
## $ Series_Complete_Janssen <dbl> 477185, 232189, 326034, 416641,~
## $ Series_Complete_Moderna <dbl> 2152155, 1299423, 1287594, 2139~
## $ Series_Complete_Pfizer <dbl> 3571765, 2103546, 2215307, 3333~
## $ Series_Complete_Unk_Manuf <dbl> 144, 11426, 1447, 1082, 34, 0, ~
## $ Series_Complete_Janssen_12Plus <dbl> 477158, 232135, 326016, 416612,~
## $ Series_Complete_Moderna_12Plus <dbl> 2152040, 1299371, 1287540, 2138~
## $ Series_Complete_Pfizer_12Plus <dbl> 3381836, 2025317, 2036626, 3144~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 143, 11362, 1431, 1073, 34, 0, ~
## $ Series_Complete_Janssen_18Plus <dbl> 475728, 231891, 325496, 416312,~
## $ Series_Complete_Moderna_18Plus <dbl> 2149019, 1298802, 1285260, 2138~
## $ Series_Complete_Pfizer_18Plus <dbl> 2997656, 1833427, 1770066, 2799~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 139, 11257, 1388, 986, 34, 0, 5~
## $ Series_Complete_Janssen_65Plus <dbl> 54321, 35691, 50477, 70861, 498~
## $ Series_Complete_Moderna_65Plus <dbl> 720300, 474871, 369087, 768663,~
## $ Series_Complete_Pfizer_65Plus <dbl> 723999, 439839, 456889, 698286,~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 65, 5936, 351, 592, 21, 0, 2511~
## $ Additional_Doses <dbl> 1544360, 1529958, 2125396, 2985~
## $ Additional_Doses_Vax_Pct <dbl> 24.9, 42.0, 55.5, 50.7, 39.8, 2~
## $ Additional_Doses_12Plus <dbl> 1544252, 1529687, 2125156, 2985~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 25.7, 42.9, 58.2, 52.4, 41.2, 2~
## $ Additional_Doses_18Plus <dbl> 1500845, 1502838, 2049913, 2906~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 26.7, 44.5, 60.6, 54.3, 43.1, 2~
## $ Additional_Doses_50Plus <dbl> 1017165, 1059552, 1281534, 1976~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 33.8, 56.3, 72.6, 64.9, 54.8, 4~
## $ Additional_Doses_65Plus <dbl> 578981, 632802, 708477, 1135879~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 38.6, 66.2, 80.8, 73.8, 62.9, 5~
## $ Additional_Doses_Moderna <dbl> 680325, 649042, 857058, 1316220~
## $ Additional_Doses_Pfizer <dbl> 836934, 856740, 1239887, 162309~
## $ Additional_Doses_Janssen <dbl> 27082, 20983, 28141, 46218, 254~
## $ Additional_Doses_Unk_Manuf <dbl> 19, 3193, 310, 106, 9, 22, 648,~
## $ Administered_Dose1_Recip_5Plus <dbl> 8594663, 4179589, 4181728, 6576~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 87.0, 65.1, 79.1, 69.8, 80.3, 0~
## $ Series_Complete_5Plus <dbl> 6200658, 3646444, 3829701, 5889~
## $ Series_Complete_5PlusPop_Pct <dbl> 62.8, 56.8, 72.4, 62.5, 64.1, 0~
## $ Administered_5Plus <dbl> 16037693, 9550281, 9850893, 150~
## $ Admin_Per_100k_5Plus <dbl> 162353, 148745, 186287, 160139,~
## $ Distributed_Per_100k_5Plus <dbl> 210004, 189797, 225320, 211315,~
## $ Series_Complete_Moderna_5Plus <dbl> 2152112, 1299406, 1287586, 2138~
## $ Series_Complete_Pfizer_5Plus <dbl> 3571235, 2103464, 2214653, 3333~
## $ Series_Complete_Janssen_5Plus <dbl> 477168, 232150, 326019, 416627,~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 143, 11424, 1443, 1081, 34, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.84e+10 3.17e+8 7.79e+7 910251 44781
## 2 after 1.83e+10 3.15e+8 7.73e+7 905741 38709
## 3 pctchg 4.83e- 3 4.32e-3 6.93e-3 0.00495 0.136
##
##
## Processed for cdcDaily:
## Rows: 38,709
## Columns: 6
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-05-13~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "NC",~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 280182~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408, 55~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 537, ~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, 11, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.51e+7 3.88e+7 945228 38675
## 2 after 4.49e+7 3.86e+7 927959 37083
## 3 pctchg 4.84e-3 4.63e-3 0.0183 0.0412
##
##
## Processed for cdcHosp:
## Rows: 37,083
## Columns: 5
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "HI", "NE", "IA", "NH", "HI", "DC", "KS", "NM", "ME", "NE",~
## $ inp <dbl> 111, 376, 497, 45, 110, 166, 474, 189, 23, 316, 546, 3246, ~
## $ hosp_adult <dbl> 111, 367, 487, 44, 108, 149, 454, 186, 23, 315, 534, 3104, ~
## $ hosp_ped <dbl> 0, 9, 10, 1, 2, 17, 5, 3, 0, 6, 12, 55, 8, 0, 1, 8, 2, 8, 6~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.66e+11 1.13e+11 1003870. 3.03e+10 1559557. 1.06e+11 1202494.
## 2 after 1.28e+11 5.46e+10 843159. 1.46e+10 1396120. 5.14e+10 1020516.
## 3 pctchg 5.20e- 1 5.16e- 1 0.160 5.16e- 1 0.105 5.17e- 1 0.151
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 22,083
## Columns: 9
## $ date <date> 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-1~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "OH", "MT", "WV", "VA", "IA"~
## $ vxa <dbl> 16040239, 9551129, 9853584, 15086338, 1349798, 17152418, 1~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889772, 527824, 6712161, 59625~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 57.4, 55.8, 56.6, 71.7, 60.9~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 1538402, 140420, 1779459, 175563,~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 87.0, 85.0, 83.5, 91.2, 92.0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355218, 476217, 6118597, 54652~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 67.2, 65.0, 65.8, 81.0, 71.7~
##
## Integrated per capita data file:
## Rows: 38,973
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220220, ovrWriteError=FALSE)The latest hospital data are downloaded:
# Run for latest data, save as RDS
indivHosp_20220221 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220221.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 399,863
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 7503
## 2 Critical Access Hospitals 106952
## 3 Long Term 27474
## 4 Short Term 257934
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 25
## 2 GU 160
## 3 MP 80
## 4 PR 4400
## 5 VI 160
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 11667 387469 727 399863
## 2 all_adult_hospital_inpatient_bed_occupi~ 3328 364400 32135 399863
## 3 icu_beds_used_7_day_avg 1649 350757 47457 399863
## 4 inpatient_beds_7_day_avg 1730 396567 1566 399863
## 5 staffed_icu_adult_patients_confirmed_an~ 4251 279744 115868 399863
## 6 total_adult_patients_hospitalized_confi~ 2372 278715 118776 399863
## 7 total_beds_7_day_avg 6632 392858 373 399863
## 8 total_icu_beds_7_day_avg 2064 377884 19915 399863## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220221, ovrWriteError=FALSE)The post-processing capabilities are included:
# Create pivoted burden data
burdenPivotList_220220 <- postProcessCDCDaily(cdc_daily_220220,
dataThruLabel="Jan 2022",
keyDatesBurden=c("2022-01-31", "2021-07-31",
"2021-01-31", "2020-07-31"
),
keyDatesVaccine=c("2021-12-31", "2021-09-30",
"2021-06-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
The hospital summaries are also added:
# Can be run only as-needed
dfStateAgeBucket2019 <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
bucketPopStateAge(popVar="pop2019")## Rows: 13572 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): SUMLEV, NAME
## dbl (16): REGION, DIVISION, STATE, SEX, AGE, ESTBASE2010_CIV, POPEST2010_CIV...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: NAME SEX AGE
##
## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## PASSED CHECK: United States total is the sum of states and DC
##
##
## PASSED CHECK: Age 999 total is the sum of the ages
##
##
## PASSED CHECK: Sex 0 total is the sum of the sexes
# Create hospitalized per capita data
hospPerCap_220220 <- hospAgePerCapita(dfStateAgeBucket2019,
lst=burdenPivotList_220220,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
The one-page CFR plot capability is included:
# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70),
"LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80),
"CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100),
"IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
)
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_220220$dfPivot,
keyState=x$keyState,
minDate=x$minDate,
multDeath=x$multDeath
)
)
The peaks and valleys plots are included:
# Burden data
cdc_daily_220220$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 3,107 × 11
## date regn new_d…¹ new_c…² inp new_d…³ new_c…⁴ inp_i…⁵ new_d…⁶
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-01-01 North Centr… NA NA NA FALSE FALSE FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE FALSE FALSE
## 4 2020-01-02 North Centr… NA NA NA FALSE FALSE FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE FALSE FALSE
## 7 2020-01-03 North Centr… NA NA NA FALSE FALSE FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE FALSE FALSE
## 10 2020-01-04 North Centr… 0 0 0 FALSE FALSE FALSE FALSE
## # … with 3,097 more rows, 2 more variables: new_cases_isValley <lgl>,
## # inp_isValley <lgl>, and abbreviated variable names ¹new_deaths, ²new_cases,
## # ³new_deaths_isPeak, ⁴new_cases_isPeak, ⁵inp_isPeak, ⁶new_deaths_isValley
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# Vaccinations data for states with 8+ million population
cdc_daily_220220$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 21-day mean administered (000)",
"vxc"="Rolling 21-day mean completed (000)"
)
)## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 5,364 × 8
## date state vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 5,354 more rows
## # ℹ Use `print(n = ...)` to see more rowsThe hospital utlization plots are included:
indivHosp_20220221 %>%
filter(state %in% c(state.abb, "DC"),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20220221, keyHosp=., plotTitle="US Hospitals Summed")
Imputed hospital utilization data are also created, using functional form:
# Impute values for hospital capacity
imputeNACapacity <- function(df,
keyStates=c(state.abb, "DC"),
varMapper=hhsMapper,
varsToImpute=c("total_beds", "adult_beds"),
varUsedToImpute=c("inpatient_beds")
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# keyState: states to include for filtering
# varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
# varsToImpute: variables to be imputed
# varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
df %>%
filter(state %in% all_of(keyStates)) %>%
colSelector(c("state", "collection_week", "hospital_pk", names(varMapper))) %>%
colRenamer(varMapper) %>%
mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), NA, ifelse(x==-999999, NA, x)))) %>%
arrange(hospital_pk, collection_week) %>%
group_by(hospital_pk) %>%
mutate(across(all_of(varsToImpute),
.fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=-999999)
)
) %>%
group_by(state, collection_week) %>%
summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}
modStateHosp_20220221 <- imputeNACapacity(indivHosp_20220221)The function is split so that it is more generic:
# Select and filter as needed
skinnyHHS <- function(df,
keyStates=c(state.abb, "DC"),
idCols=c("state", "collection_week", "hospital_pk"),
varMapper=hhsMapper
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# keyState: states to include for filtering
# varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
df %>%
filter(state %in% all_of(keyStates)) %>%
colSelector(c(all_of(idCols), names(varMapper))) %>%
colRenamer(varMapper)
}
# Impute values for hospital capacity
imputeNACapacity <- function(df,
extraNA=c(-999999),
convertAllNA=TRUE,
idVars=c("hospital_pk"),
sortVars=c("collection_week"),
varsToImpute=c("total_beds", "adult_beds"),
varUsedToImpute=c("inpatient_beds")
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# extraNA: values that should be treated as NA
# convertAllNA: boolean, should all extraNA values be converted in all numeric columns?
# if FALSE, extraNA values will not be converted, though imputing will treat as NA
# varsToImpute: variables to be imputed
# varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
# Convert NA if requested
if(isTRUE(convertAllNA)) {
df <- df %>%
mutate(across(where(is.numeric),
.fns=function(x) ifelse(is.na(x), NA, ifelse(x %in% all_of(extraNA), NA, x))
)
)
}
# Impute values and return data
df %>%
arrange(across(all_of(c(idVars, sortVars)))) %>%
group_by(across(all_of(idVars))) %>%
mutate(across(all_of(varsToImpute),
.fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=extraNA)
)
) %>%
ungroup()
}
sumImputedHHS <- function(df,
groupVars=c("state", "collection_week")) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# groupVars: variables for summing the data to
df %>%
group_by(across(all_of(groupVars))) %>%
summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}
identical(skinnyHHS(indivHosp_20220221) %>%
imputeNACapacity() %>%
sumImputedHHS(),
modStateHosp_20220221
)## [1] TRUEUpdated maps with imputed capacity are created:
modStateHosp_20220221 <- skinnyHHS(indivHosp_20220221) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220221,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to January 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
createGeoMap(modStateHosp_20220221 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to January 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
Example Data Update
The function is run to download and process the latest data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220304.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220304.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220304.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220220")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220220")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220220")$dfRaw$vax
)
cdc_daily_220304 <- readRunCDCDaily(thruLabel="Mar 2, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-03-03 tot_cases 175 188 13 0.07162534
## 2 2022-02-13 new_deaths 615 446 169 0.31856739
## 3 2022-02-06 new_deaths 609 472 137 0.25346901
## 4 2022-02-12 new_deaths 891 695 196 0.24716267
## 5 2022-02-05 new_deaths 1158 989 169 0.15742897
## 6 2022-01-30 new_deaths 869 796 73 0.08768769
## 7 2022-02-07 new_deaths 3177 3000 177 0.05730937
## 8 2022-02-08 new_deaths 3704 3504 200 0.05549390
## 9 2022-02-03 new_deaths 2653 2515 138 0.05340557
## 10 2022-01-29 new_deaths 1469 1394 75 0.05239260
## 11 2022-02-11 new_deaths 2775 2638 137 0.05061888
## 12 2020-03-03 new_cases 51 64 13 0.22608696
## 13 2022-02-12 new_cases 66377 55089 11288 0.18586271
## 14 2022-02-13 new_cases 47803 40950 6853 0.15442858
## 15 2022-01-30 new_cases 155259 138089 17170 0.11706233
## 16 2022-02-05 new_cases 102256 91295 10961 0.11326214
## 17 2022-02-14 new_cases 178028 199342 21314 0.11296075
## 18 2022-02-11 new_cases 155537 172496 16959 0.10339813
## 19 2022-01-29 new_cases 215839 195076 20763 0.10105740
## 20 2020-03-07 new_cases 146 160 14 0.09150327
## 21 2021-10-31 new_cases 22766 20850 1916 0.08785767
## 22 2021-11-06 new_cases 32140 29452 2688 0.08728406
## 23 2021-10-24 new_cases 25952 23899 2053 0.08236545
## 24 2021-11-07 new_cases 28368 26379 1989 0.07266152
## 25 2020-03-06 new_cases 130 121 9 0.07171315
## 26 2021-10-23 new_cases 33628 31349 2279 0.07014790
## 27 2022-01-22 new_cases 320403 299989 20414 0.06581000
## 28 2022-02-06 new_cases 96184 90271 5913 0.06342549
## 29 2022-01-18 new_cases 861976 917498 55522 0.06240271
## 30 2020-03-09 new_cases 390 415 25 0.06211180
## 31 2022-01-31 new_cases 583405 620416 37011 0.06148921
## 32 2022-01-23 new_cases 310096 291779 18317 0.06086646
## 33 2021-11-14 new_cases 30649 28992 1657 0.05556580
## 34 2021-11-20 new_cases 42759 40531 2228 0.05349982
## 35 2021-12-25 new_cases 126095 119545 6550 0.05333008
## 36 2021-10-30 new_cases 31410 29822 1588 0.05186830
## 37 2021-05-24 new_cases 15400 16206 806 0.05100297
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 RI tot_deaths 1395734 1419362 23628 0.016786639
## 2 FL tot_deaths 22083833 22194156 110323 0.004983198
## 3 KY tot_deaths 4208985 4192009 16976 0.004041427
## 4 NC tot_deaths 7123584 7111646 11938 0.001677247
## 5 AL tot_deaths 6231481 6222060 9421 0.001512983
## 6 RI tot_cases 75530417 79533518 4003101 0.051631619
## 7 ME tot_cases 38751314 36951188 1800126 0.047557900
## 8 WA tot_cases 263650336 264272454 622118 0.002356852
## 9 KY tot_cases 270113183 269767213 345970 0.001281654
## 10 AL new_deaths 18381 17877 504 0.027800761
## 11 FL new_deaths 70406 68581 1825 0.026261449
## 12 WA new_deaths 11615 11316 299 0.026078235
## 13 KY new_deaths 13885 13565 320 0.023315118
## 14 NC new_deaths 22277 22148 129 0.005807541
## 15 ME new_cases 225203 212435 12768 0.058349595
## 16 RI new_cases 336543 354045 17502 0.050687240
## 17 WA new_cases 1396813 1410596 13783 0.009819018
## 18 KY new_cases 1265367 1258310 7057 0.005592633
## 19 SD new_cases 234285 234961 676 0.002881218
## 20 NC new_cases 2563976 2559793 4183 0.001632782
## 21 SC new_cases 1451483 1449247 2236 0.001541681
##
##
##
## Raw file for cdcDaily:
## Rows: 46,260
## Columns: 15
## $ date <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-0~
## $ state <chr> "KS", "UT", "CO", "AR", "AR", "CO", "PW", "UT", "MA", "~
## $ tot_cases <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 0, 636992, 7~
## $ conf_cases <dbl> 241035, 359641, 411869, NA, NA, 1117524, NA, 636992, 65~
## $ prob_cases <dbl> 56194, 0, 26876, NA, NA, 105369, NA, 0, 45550, 321, NA,~
## $ new_cases <dbl> 0, 1060, 677, 0, 547, 6962, 0, 0, 451, 619, 69, 24010, ~
## $ pnew_case <dbl> 0, 0, 60, NA, 0, 1247, 0, 0, 46, 1, 10, 4196, 264, 3202~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 0, 3787, 17818, 805, 8~
## $ conf_death <dbl> NA, 1729, 5218, NA, NA, 9666, NA, 3635, 17458, 624, NA,~
## $ prob_death <dbl> NA, 56, 734, NA, NA, 1287, NA, 152, 360, 181, NA, NA, 1~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 0, 5, 3, 0, 345, 8, 190, 0, 3, ~
## $ pnew_death <dbl> 0, 2, 0, NA, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, NA,~
## $ created_at <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "03~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Not agree", "Not agree", "A~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "Not agree", "Not agree", "Agr~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 11
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-20 inp 58908 62620 3712 0.06108880
## 2 2022-02-20 hosp_adult 56750 60478 3728 0.06360255
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 110569 109813 756 0.006860814
## 2 WV hosp_ped 4497 4703 206 0.044782609
## 3 ME hosp_ped 1594 1538 56 0.035759898
## 4 MA hosp_ped 10034 9724 310 0.031379694
## 5 IN hosp_ped 15429 15261 168 0.010948192
## 6 KY hosp_ped 15750 15913 163 0.010295929
## 7 VA hosp_ped 14705 14555 150 0.010252905
## 8 NJ hosp_ped 16251 16415 164 0.010041021
## 9 NV hosp_ped 4105 4067 38 0.009300049
## 10 SC hosp_ped 7732 7661 71 0.009224972
## 11 AL hosp_ped 17872 17976 104 0.005802276
## 12 VT hosp_ped 360 362 2 0.005540166
## 13 KS hosp_ped 4025 4005 20 0.004981320
## 14 NM hosp_ped 6428 6457 29 0.004501358
## 15 IA hosp_ped 6509 6481 28 0.004311008
## 16 NH hosp_ped 761 758 3 0.003949967
## 17 FL hosp_ped 82260 82509 249 0.003022413
## 18 TN hosp_ped 18633 18581 52 0.002794647
## 19 WY hosp_ped 784 786 2 0.002547771
## 20 CO hosp_ped 18126 18084 42 0.002319801
## 21 SD hosp_ped 3899 3891 8 0.002053915
## 22 GA hosp_ped 43658 43742 84 0.001922197
## 23 AR hosp_ped 10931 10911 20 0.001831334
## 24 UT hosp_ped 7634 7621 13 0.001704359
## 25 CT hosp_ped 5640 5649 9 0.001594472
## 26 HI hosp_ped 2016 2019 3 0.001486989
## 27 MS hosp_ped 9380 9368 12 0.001280137
## 28 AZ hosp_ped 23979 23949 30 0.001251878
## 29 IL hosp_ped 36164 36121 43 0.001189735
## 30 MN hosp_ped 13210 13224 14 0.001059242
## 31 ND hosp_adult 104829 102042 2787 0.026944328
##
##
##
## Raw file for cdcHosp:
## Rows: 39,269
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 28,760
## Columns: 82
## $ date <date> 2022-03-03, 2022-03-03, 2022-0~
## $ MMWR_week <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9~
## $ state <chr> "NE", "NC", "TX", "CA", "AL", "~
## $ Distributed <dbl> 3775510, 20928600, 58996495, 86~
## $ Distributed_Janssen <dbl> 149600, 917900, 2609300, 368570~
## $ Distributed_Moderna <dbl> 1331380, 7886660, 21192040, 307~
## $ Distributed_Pfizer <dbl> 2294530, 12124040, 35195155, 51~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 195177, 199546, 203465, 217827,~
## $ Distributed_Per_100k_12Plus <dbl> 233430, 232915, 244787, 255803,~
## $ Distributed_Per_100k_18Plus <dbl> 258892, 255621, 273182, 281107,~
## $ Distributed_Per_100k_65Plus <dbl> 1208330, 1195170, 1579880, 1474~
## $ vxa <dbl> 3086667, 16146189, 44500682, 71~
## $ Administered_12Plus <dbl> 2984836, 15667506, 42929166, 69~
## $ Administered_18Plus <dbl> 2784330, 14709026, 39448235, 63~
## $ Administered_65Plus <dbl> 818837, 4254447, 8966187, 14693~
## $ Administered_Janssen <dbl> 93421, 510563, 1535569, 2278802~
## $ Administered_Moderna <dbl> 1110596, 6003546, 16331212, 267~
## $ Administered_Pfizer <dbl> 1876552, 9631145, 26629492, 426~
## $ Administered_Unk_Manuf <dbl> 6098, 935, 4409, 15243, 477, 21~
## $ Admin_Per_100k <dbl> 159566, 153948, 153472, 181390,~
## $ Admin_Per_100k_12Plus <dbl> 184544, 174364, 178121, 205442,~
## $ Admin_Per_100k_18Plus <dbl> 190925, 179655, 182664, 208987,~
## $ Admin_Per_100k_65Plus <dbl> 262063, 242959, 240108, 251683,~
## $ Recip_Administered <dbl> 3099534, 16045766, 43251399, 71~
## $ Administered_Dose1_Recip <dbl> 1343086, 8641769, 20646737, 323~
## $ Administered_Dose1_Pop_Pct <dbl> 69.4, 82.4, 71.2, 81.8, 61.9, 6~
## $ Administered_Dose1_Recip_12Plus <dbl> 1287672, 8369683, 19753177, 309~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 79.6, 93.1, 82.0, 91.9, 71.1, 7~
## $ Administered_Dose1_Recip_18Plus <dbl> 1192672, 7856851, 17992902, 284~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.8, 95.0, 83.3, 92.9, 73.8, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 306831, 2160730, 3621726, 59741~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 1210303, 6231369, 17444705, 278~
## $ vxcpoppct <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 5~
## $ Series_Complete_12Plus <dbl> 1164264, 6032933, 16838920, 267~
## $ Series_Complete_12PlusPop_Pct <dbl> 72.0, 67.1, 69.9, 79.5, 58.0, 6~
## $ vxcgte18 <dbl> 1078877, 5641201, 15431387, 245~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 6~
## $ vxcgte65 <dbl> 284820, 1501131, 3209108, 51872~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 8~
## $ Series_Complete_Janssen <dbl> 87478, 478497, 1339798, 2070288~
## $ Series_Complete_Moderna <dbl> 412033, 2159277, 6030460, 95834~
## $ Series_Complete_Pfizer <dbl> 709218, 3593448, 10073548, 1620~
## $ Series_Complete_Unk_Manuf <dbl> 1574, 147, 899, 4865, 654, 375,~
## $ Series_Complete_Janssen_12Plus <dbl> 87453, 478469, 1339351, 2069676~
## $ Series_Complete_Moderna_12Plus <dbl> 411994, 2159160, 6029642, 95826~
## $ Series_Complete_Pfizer_12Plus <dbl> 663259, 3395158, 9469060, 15076~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 1558, 146, 867, 4805, 654, 373,~
## $ Series_Complete_Janssen_18Plus <dbl> 87386, 477036, 1337802, 2062395~
## $ Series_Complete_Moderna_18Plus <dbl> 411823, 2156126, 6025533, 95567~
## $ Series_Complete_Pfizer_18Plus <dbl> 578185, 3007897, 8067213, 12955~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 1483, 142, 839, 4500, 650, 366,~
## $ Series_Complete_Janssen_65Plus <dbl> 6942, 54470, 177453, 201180, 36~
## $ Series_Complete_Moderna_65Plus <dbl> 138470, 721440, 1524709, 262127~
## $ Series_Complete_Pfizer_65Plus <dbl> 138496, 725155, 1506622, 236331~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 912, 66, 324, 1458, 415, 213, 1~
## $ Additional_Doses <dbl> 585237, 1574890, 6257276, 13507~
## $ Additional_Doses_Vax_Pct <dbl> 48.4, 25.3, 35.9, 48.5, 34.4, 3~
## $ Additional_Doses_12Plus <dbl> 585134, 1574750, 6256853, 13506~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 50.3, 26.1, 37.2, 50.5, 34.9, 3~
## $ Additional_Doses_18Plus <dbl> 565479, 1527656, 6053696, 12967~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 52.4, 27.1, 39.2, 52.8, 36.3, 4~
## $ Additional_Doses_50Plus <dbl> 364760, 1031758, 3720211, 72429~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 65.5, 34.3, 52.0, 63.8, 47.2, 5~
## $ Additional_Doses_65Plus <dbl> 211563, 585640, 1945440, 368152~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 74.3, 39.0, 60.6, 71.0, 56.9, 6~
## $ Additional_Doses_Moderna <dbl> 229241, 693169, 2749043, 586417~
## $ Additional_Doses_Pfizer <dbl> 349233, 854191, 3412442, 742991~
## $ Additional_Doses_Janssen <dbl> 6431, 27508, 95589, 213372, 152~
## $ Additional_Doses_Unk_Manuf <dbl> 332, 22, 202, 522, 81, 490, 73,~
## $ Administered_Dose1_Recip_5Plus <dbl> 1342804, 8639422, 20641353, 323~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 74.5, 87.5, 76.4, 87.0, 65.9, 7~
## $ Series_Complete_5Plus <dbl> 1210243, 6230533, 17443348, 278~
## $ Series_Complete_5PlusPop_Pct <dbl> 67.1, 63.1, 64.6, 75.0, 53.5, 5~
## $ Administered_5Plus <dbl> 3086314, 16143045, 44494008, 71~
## $ Admin_Per_100k_5Plus <dbl> 171126, 163419, 164762, 192973,~
## $ Distributed_Per_100k_5Plus <dbl> 209340, 211864, 218465, 231812,~
## $ Series_Complete_Moderna_5Plus <dbl> 412011, 2159234, 6029941, 95831~
## $ Series_Complete_Pfizer_5Plus <dbl> 709196, 3592673, 10072988, 1620~
## $ Series_Complete_Janssen_5Plus <dbl> 87463, 478480, 1339521, 2069915~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 1573, 146, 898, 4864, 654, 373,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.93e+10 3.28e+8 7.85e+7 931901 45489
## 2 after 1.92e+10 3.27e+8 7.80e+7 927317 39321
## 3 pctchg 4.93e- 3 4.33e-3 6.98e-3 0.00492 0.136
##
##
## Processed for cdcDaily:
## Rows: 39,321
## Columns: 6
## $ date <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-08-22~
## $ state <chr> "KS", "UT", "CO", "AR", "AR", "CO", "UT", "MA", "HI", "TX",~
## $ tot_cases <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 636992, 704796, ~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 3787, 17818, 883, 33124, 7~
## $ new_cases <dbl> 0, 1060, 677, 0, 547, 6962, 0, 451, 69, 24010, 1028, 18811,~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 5, 0, 345, 8, 190, 3, 15, 7, 8, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.57e+7 3.93e+7 965351 39269
## 2 after 4.54e+7 3.91e+7 947960 37644
## 3 pctchg 4.81e-3 4.60e-3 0.0180 0.0414
##
##
## Processed for cdcHosp:
## Rows: 37,644
## Columns: 5
## $ date <date> 2020-10-18, 2020-10-13, 2020-10-12, 2020-10-08, 2020-10-06~
## $ state <chr> "VT", "NH", "ID", "MT", "HI", "NH", "NC", "DC", "MA", "MT",~
## $ inp <dbl> 2, 34, 221, 262, 124, 48, 1283, 156, 354, 207, 116, 102, 39~
## $ hosp_adult <dbl> 2, 34, 219, 259, 124, 48, 1246, 141, 347, 206, 109, 101, 38~
## $ hosp_ped <dbl> 0, 0, 2, 3, 0, 0, 34, 15, 7, 1, 3, 1, 10, 0, 0, 1, 6, 6, 7,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.79e+11 1.18e+11 1050491. 3.15e+10 1622354. 1.11e+11 1255957.
## 2 after 1.34e+11 5.72e+10 882060. 1.52e+10 1450421 5.37e+10 1065505.
## 3 pctchg 5.20e- 1 5.16e- 1 0.160 5.16e- 1 0.106 5.17e- 1 0.152
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 22,695
## Columns: 9
## $ date <date> 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-0~
## $ state <chr> "NE", "NC", "TX", "CA", "AL", "SC", "WV", "MN", "CO", "KS"~
## $ vxa <dbl> 3086667, 16146189, 44500682, 71671126, 6108052, 7287794, 2~
## $ vxc <dbl> 1210303, 6231369, 17444705, 27867605, 2466221, 2880832, 10~
## $ vxcpoppct <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 56.0, 56.8, 68.3, 69.3, 60.3~
## $ vxcgte65 <dbl> 284820, 1501131, 3209108, 5187220, 689667, 807207, 306687,~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 86.1, 83.6, 95.0, 92.0, 89.5~
## $ vxcgte18 <dbl> 1078877, 5641201, 15431387, 24579521, 2300814, 2646739, 94~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 65.6, 66.0, 78.2, 79.1, 71.1~
##
## Integrated per capita data file:
## Rows: 39,534
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220304, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220304 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220304.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 409,797
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 7690
## 2 Critical Access Hospitals 109641
## 3 Long Term 28161
## 4 Short Term 264305
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 27
## 2 GU 164
## 3 MP 82
## 4 PR 4506
## 5 VI 164
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 15604 393445 748 409797
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 373556 32923 409797
## 3 icu_beds_used_7_day_avg 1649 359635 48513 409797
## 4 inpatient_beds_7_day_avg 1730 406462 1605 409797
## 5 staffed_icu_adult_patients_confirmed_an~ 4241 286438 119118 409797
## 6 total_adult_patients_hospitalized_confi~ 2362 285557 121878 409797
## 7 total_beds_7_day_avg 10392 399022 383 409797
## 8 total_icu_beds_7_day_avg 2064 387368 20365 409797## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220304, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220304 <- postProcessCDCDaily(cdc_daily_220304,
dataThruLabel="Feb 2022",
keyDatesBurden=c("2022-02-28", "2021-08-31",
"2021-02-28", "2020-08-31"
),
keyDatesVaccine=c("2022-02-28", "2021-10-31",
"2021-06-30", "2021-02-28"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220304 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220304,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
Peaks and valleys are converted to functional form:
peakValleyCDCDaily <- function(df,
burdenVars=c("new_deaths", "new_cases", "inp"),
burdenWidth=71,
burdenRollMean=7,
minPopVax=8000000,
vaxVars=c("vxa", "vxc"),
minDateVax="2020-12-01",
vaxWidth=71,
vaxRollMean=21
) {
# FUNCTION ARGUMENTS
# df: data frame (can also pass a list that contains data frame "dfPerCapita")
# burdenVars: variables to be used for burden peaks and valleys
# burdenWidth: window size to be used for burden data
# burdenRollMean: rolling mean to use for smoothing burden data
# minPopVax: minimum population for state vaccines to be plotted
# vaxVars: variables to be used for vaccines peaks and valleys
# minDateVax: earliest day to use for vaccines plotting
# vaxWidth: window size to be used for vaccines data
# vaxRollMean: rolling mean to use for smoothing vaccines data
# Only works for specified burdenVars and vaxVars (fix)
if(!all.equal(sort(burdenVars), sort(c("new_deaths", "new_cases", "inp")))) stop("\nNot yet enabled - burden\n")
if(!all.equal(sort(vaxVars), sort(c("vxa", "vxc")))) stop("\nNot yet enabled - vaccines\n")
# Extract data frame from df if needed
if("list" %in% class(df)) df <- df[["dfPerCapita"]]
# Burden data
df %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=burdenVars,
windowWidth = burdenWidth,
rollMean=burdenRollMean,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"=paste0("Rolling ", burdenRollMean, "-day mean deaths"),
"new_cases"=paste0("Rolling ", burdenRollMean, "-day mean cases (000)"),
"inp"=paste0("Rolling ", burdenRollMean, "-day mean in hospital (000)")
)
)
# Vaccinations data for states with at least threshold population
df %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= minPopVax) %>%
select(c("state", "date", all_of(vaxVars))) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= minDateVax) %>%
makePeakValley(numVar=vaxVars,
windowWidth = vaxWidth,
rollMean=vaxRollMean,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", minPopVax, ")"),
"vxc"=paste0("Became fully vaccinated (states with population >= ", minPopVax, ")")
),
yLab=c("vxa"=paste0("Rolling ", vaxRollMean, "-day mean administered (000)"),
"vxc"=paste0("Rolling ", vaxRollMean,"-day mean completed (000)")
)
)
}
peakValleyCDCDaily(cdc_daily_220304)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 5,496 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 5,486 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220304 <- skinnyHHS(indivHosp_20220304) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220304,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to February 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
createGeoMap(modStateHosp_20220304 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to February 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220416.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220416.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220416.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220304")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220304")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220304")$dfRaw$vax
)
cdc_daily_220416 <- readRunCDCDaily(thruLabel="Apr 14, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-27 new_deaths 336 207 129 0.47513812
## 2 2022-02-26 new_deaths 553 416 137 0.28276574
## 3 2022-02-20 new_deaths 563 441 122 0.24302789
## 4 2021-07-30 new_deaths 651 521 130 0.22184300
## 5 2022-02-19 new_deaths 732 612 120 0.17857143
## 6 2022-02-13 new_deaths 693 615 78 0.11926606
## 7 2022-02-21 new_deaths 1089 967 122 0.11867704
## 8 2022-02-12 new_deaths 988 891 97 0.10324641
## 9 2022-02-06 new_deaths 674 609 65 0.10132502
## 10 2022-02-18 new_deaths 2283 2149 134 0.06046931
## 11 2022-02-05 new_deaths 1221 1158 63 0.05296343
## 12 2022-02-26 new_cases 26248 23158 3090 0.12508602
## 13 2021-10-31 new_cases 25456 22766 2690 0.11156733
## 14 2022-02-27 new_cases 18268 16411 1857 0.10709651
## 15 2022-02-28 new_cases 72092 80046 7954 0.10456296
## 16 2021-11-07 new_cases 31372 28368 3004 0.10056913
## 17 2021-11-06 new_cases 35485 32140 3345 0.09892791
## 18 2021-10-30 new_cases 34475 31410 3065 0.09304090
## 19 2021-11-14 new_cases 33631 30649 2982 0.09278158
## 20 2021-10-23 new_cases 36520 33628 2892 0.08245424
## 21 2021-10-24 new_cases 28146 25952 2194 0.08111206
## 22 2021-11-20 new_cases 45749 42759 2990 0.06756451
## 23 2021-11-21 new_cases 38274 35892 2382 0.06423429
## 24 2021-11-13 new_cases 53584 50305 3279 0.06312507
## 25 2021-10-25 new_cases 84093 88971 4878 0.05637221
## 26 2021-11-08 new_cases 116560 122589 6029 0.05042045
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 4418134 4375794 42340 0.009629372
## 2 FL tot_deaths 23037457 22932268 105189 0.004576447
## 3 AL tot_deaths 6469661 6452309 17352 0.002685659
## 4 NC tot_deaths 7407674 7392943 14731 0.001990593
## 5 SC tot_deaths 5380045 5370547 9498 0.001766972
## 6 CO tot_cases 314009823 311159444 2850379 0.009118743
## 7 DE tot_cases 61116141 61473234 357093 0.005825839
## 8 KY tot_cases 286467313 285415770 1051543 0.003677475
## 9 NC tot_cases 592997222 592074229 922993 0.001557700
## 10 KY new_deaths 14951 13935 1016 0.070345496
## 11 DE new_deaths 2711 2573 138 0.052233157
## 12 AL new_deaths 19167 18407 760 0.040453505
## 13 FL new_deaths 72517 70789 1728 0.024116227
## 14 NC new_deaths 22958 22671 287 0.012579719
## 15 RI new_deaths 3441 3413 28 0.008170411
## 16 CO new_cases 1345585 1312298 33287 0.025047754
## 17 KY new_cases 1305049 1282281 22768 0.017599610
## 18 NC new_cases 2612332 2592991 19341 0.007431239
## 19 DE new_cases 257219 256051 1168 0.004551211
## 20 SC new_cases 1463332 1461843 1489 0.001018059
##
##
##
## Raw file for cdcDaily:
## Rows: 48,840
## Columns: 15
## $ date <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state <chr> "KS", "AS", "AR", "MP", "AS", "HI", "MA", "PR", "GA", "~
## $ tot_cases <dbl> 621273, 11, 56199, 37, 0, 661, 704796, 35112, 1187107, ~
## $ conf_cases <dbl> 470516, NA, NA, 37, NA, NA, 659246, 34791, 937515, 3739~
## $ prob_cases <dbl> 150757, NA, NA, 0, NA, NA, 45550, 321, 249592, 101649, ~
## $ new_cases <dbl> 19414, 0, 547, 1, 0, 8, 451, 619, 3829, 1028, 0, 0, 276~
## $ pnew_case <dbl> 6964, 0, 0, 0, 0, 0, 46, 1, 1144, 264, 0, 0, 317, 0, 0,~
## $ tot_deaths <dbl> 7162, 0, 674, 2, 0, 17, 17818, 805, 21690, 7488, 0, 140~
## $ conf_death <dbl> NA, NA, NA, 2, NA, NA, 17458, 624, 18725, 6379, NA, 980~
## $ prob_death <dbl> NA, NA, NA, 0, NA, NA, 360, 181, 2965, 1109, NA, 4202, ~
## $ new_deaths <dbl> 21, 0, 11, 0, 0, 0, 5, 3, 7, 8, 0, 0, 3, 0, 69, 34, 0, ~
## $ pnew_death <dbl> 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ created_at <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases <chr> "Agree", NA, "Not agree", "Agree", NA, "Not agree", "Ag~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Not agree", "Agre~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-03-03 inp 39007 41066 2059 0.05142807
## 2 2022-03-03 hosp_adult 37433 39443 2010 0.05229200
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NH hosp_ped 876 785 91 0.109572547
## 2 WV hosp_ped 4828 4653 175 0.036915937
## 3 MA hosp_ped 9943 10225 282 0.027965093
## 4 AR hosp_ped 10871 11128 257 0.023364698
## 5 NV hosp_ped 4183 4271 88 0.020818547
## 6 SC hosp_ped 7993 7878 115 0.014491840
## 7 KY hosp_ped 16368 16155 213 0.013098423
## 8 AL hosp_ped 18331 18119 212 0.011632373
## 9 VA hosp_ped 14832 14994 162 0.010863005
## 10 DE hosp_ped 4271 4236 35 0.008228518
## 11 ID hosp_ped 3406 3433 27 0.007895891
## 12 NJ hosp_ped 16512 16406 106 0.006440245
## 13 IN hosp_ped 15652 15740 88 0.005606524
## 14 UT hosp_ped 7997 8031 34 0.004242575
## 15 MD hosp_ped 12691 12739 48 0.003775069
## 16 PR hosp_ped 17374 17309 65 0.003748234
## 17 OK hosp_ped 21962 22043 81 0.003681400
## 18 TN hosp_ped 19088 19153 65 0.003399493
## 19 VT hosp_ped 382 383 1 0.002614379
## 20 FL hosp_ped 83322 83126 196 0.002355090
## 21 CO hosp_ped 18479 18439 40 0.002166965
## 22 PA hosp_ped 45833 45907 74 0.001613255
## 23 HI hosp_ped 2117 2120 3 0.001416096
## 24 CA hosp_ped 67230 67137 93 0.001384268
## 25 MO hosp_ped 33571 33616 45 0.001339545
## 26 WY hosp_ped 794 795 1 0.001258653
## 27 WA hosp_ped 11473 11486 13 0.001132454
## 28 LA hosp_ped 10021 10032 11 0.001097093
## 29 NH hosp_adult 90770 90907 137 0.001508171
##
##
##
## Raw file for cdcHosp:
## Rows: 41,591
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 31,512
## Columns: 82
## $ date <date> 2022-04-15, 2022-04-15, 2022-0~
## $ MMWR_week <dbl> 15, 15, 15, 15, 15, 15, 15, 15,~
## $ state <chr> "NV", "SC", "NE", "ND", "CA", "~
## $ Distributed <dbl> 5870110, 10352975, 3914910, 136~
## $ Distributed_Janssen <dbl> 258700, 451200, 150100, 52800, ~
## $ Distributed_Moderna <dbl> 2014400, 4306940, 1372780, 5258~
## $ Distributed_Pfizer <dbl> 3597010, 5594835, 2392030, 7883~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 190578, 201079, 202383, 179382,~
## $ Distributed_Per_100k_12Plus <dbl> 223896, 234232, 242048, 214517,~
## $ Distributed_Per_100k_18Plus <dbl> 245867, 256418, 268451, 234924,~
## $ Distributed_Per_100k_65Plus <dbl> 1183560, 1104880, 1252940, 1140~
## $ vxa <dbl> 4846899, 7387622, 3169859, 1070~
## $ Administered_12Plus <dbl> 4729850, 7215781, 3059482, 1035~
## $ Administered_18Plus <dbl> 4435348, 6828252, 2853809, 9829~
## $ Administered_65Plus <dbl> 1192563, 2256911, 847795, 29325~
## $ Administered_Janssen <dbl> 187265, 231115, 95046, 39681, 2~
## $ Administered_Moderna <dbl> 1659796, 2864880, 1138222, 4068~
## $ Administered_Pfizer <dbl> 2999310, 4289420, 1930052, 6234~
## $ Administered_Unk_Manuf <dbl> 528, 2207, 6539, 325, 15816, 47~
## $ Admin_Per_100k <dbl> 157359, 143485, 163867, 140451,~
## $ Admin_Per_100k_12Plus <dbl> 180405, 163254, 189160, 162456,~
## $ Admin_Per_100k_18Plus <dbl> 185772, 169119, 195690, 168926,~
## $ Admin_Per_100k_65Plus <dbl> 240450, 240860, 271331, 244697,~
## $ Recip_Administered <dbl> 4818924, 7391493, 3182205, 1049~
## $ Administered_Dose1_Recip <dbl> 2308282, 3462294, 1356028, 4934~
## $ Administered_Dose1_Pop_Pct <dbl> 74.9, 67.2, 70.1, 64.8, 83.2, 7~
## $ Administered_Dose1_Recip_12Plus <dbl> 2242948, 3363978, 1296952, 4757~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 85.5, 76.1, 80.2, 74.7, 93.2, 8~
## $ Administered_Dose1_Recip_18Plus <dbl> 2090215, 3165363, 1200872, 4491~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 87.5, 78.4, 82.3, 77.2, 94.2, 8~
## $ Administered_Dose1_Recip_65Plus <dbl> 502147, 950807, 308194, 120897,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 1864022, 2914901, 1225968, 4170~
## $ vxcpoppct <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 6~
## $ Series_Complete_12Plus <dbl> 1813258, 2838179, 1175197, 4028~
## $ Series_Complete_12PlusPop_Pct <dbl> 69.2, 64.2, 72.7, 63.2, 80.5, 7~
## $ vxcgte18 <dbl> 1694297, 2670106, 1088392, 3803~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 7~
## $ vxcgte65 <dbl> 415049, 810448, 286617, 102377,~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 9~
## $ Series_Complete_Janssen <dbl> 172318, 207380, 88579, 36537, 2~
## $ Series_Complete_Moderna <dbl> 602890, 1039279, 415073, 145324~
## $ Series_Complete_Pfizer <dbl> 1088750, 1667852, 720640, 23513~
## $ Series_Complete_Unk_Manuf <dbl> 64, 390, 1676, 13, 5079, 1680, ~
## $ Series_Complete_Janssen_12Plus <dbl> 172314, 207331, 88553, 36530, 2~
## $ Series_Complete_Moderna_12Plus <dbl> 602884, 1039114, 415031, 145318~
## $ Series_Complete_Pfizer_12Plus <dbl> 1037996, 1591346, 669959, 22103~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 64, 388, 1654, 13, 5014, 1656, ~
## $ Series_Complete_Janssen_18Plus <dbl> 172270, 206747, 88485, 36382, 2~
## $ Series_Complete_Moderna_18Plus <dbl> 602778, 1037101, 414851, 145110~
## $ Series_Complete_Pfizer_18Plus <dbl> 919188, 1425877, 583491, 198891~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 61, 381, 1565, 12, 4699, 1605, ~
## $ Series_Complete_Janssen_65Plus <dbl> 26151, 31325, 6998, 4376, 20284~
## $ Series_Complete_Moderna_65Plus <dbl> 190706, 345825, 139225, 48336, ~
## $ Series_Complete_Pfizer_65Plus <dbl> 198156, 433078, 139467, 49660, ~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 36, 220, 927, 5, 1493, 392, 362~
## $ Additional_Doses <dbl> 713544, 1141995, 617137, 168496~
## $ Additional_Doses_Vax_Pct <dbl> 38.3, 39.2, 50.3, 40.4, 50.2, 5~
## $ Additional_Doses_12Plus <dbl> 713502, 1141860, 616974, 168479~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 39.3, 40.2, 52.5, 41.8, 52.5, 6~
## $ Additional_Doses_18Plus <dbl> 694360, 1116070, 594777, 166705~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 41.0, 41.8, 54.6, 43.8, 54.7, 6~
## $ Additional_Doses_50Plus <dbl> 458721, 822790, 381496, 114424,~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 53.8, 53.4, 68.1, 57.9, 65.5, 7~
## $ Additional_Doses_65Plus <dbl> 259411, 507047, 220977, 69677, ~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 62.5, 62.6, 77.1, 68.1, 72.7, 8~
## $ Additional_Doses_Moderna <dbl> 292543, 489262, 242700, 77363, ~
## $ Additional_Doses_Pfizer <dbl> 408783, 629984, 367091, 88613, ~
## $ Additional_Doses_Janssen <dbl> 12214, 22236, 6918, 2511, 22302~
## $ Additional_Doses_Unk_Manuf <dbl> 4, 513, 428, 9, 575, 418, 61, 9~
## $ Administered_Dose1_Recip_5Plus <dbl> 2308180, 3460120, 1355714, 4931~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 79.7, 71.3, 75.2, 69.7, 88.5, 7~
## $ Series_Complete_5Plus <dbl> 1864007, 2913784, 1225903, 4168~
## $ Series_Complete_5PlusPop_Pct <dbl> 64.4, 60.0, 68.0, 58.9, 76.2, 7~
## $ Administered_5Plus <dbl> 4846790, 7384273, 3169464, 1069~
## $ Admin_Per_100k_5Plus <dbl> 167444, 152057, 175737, 151118,~
## $ Distributed_Per_100k_5Plus <dbl> 202797, 213189, 217070, 193090,~
## $ Series_Complete_Moderna_5Plus <dbl> 602885, 1039202, 415051, 145320~
## $ Series_Complete_Pfizer_5Plus <dbl> 1088743, 1666860, 720616, 23500~
## $ Series_Complete_Janssen_5Plus <dbl> 172315, 207334, 88563, 36532, 2~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 64, 388, 1673, 13, 5078, 1675, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.28e+10 3.70e+8 7.99e+7 968928 48026
## 2 after 2.26e+10 3.68e+8 7.93e+7 964235 41514
## 3 pctchg 5.25e- 3 4.38e-3 7.20e-3 0.00484 0.136
##
##
## Processed for cdcDaily:
## Rows: 41,514
## Columns: 6
## $ date <date> 2022-01-14, 2020-08-22, 2020-06-05, 2021-05-22, 2021-08-01~
## $ state <chr> "KS", "AR", "HI", "MA", "GA", "OK", "OK", "GA", "GA", "TX",~
## $ tot_cases <dbl> 621273, 56199, 661, 704796, 1187107, 475578, 1034439, 14780~
## $ tot_deaths <dbl> 7162, 674, 17, 17818, 21690, 7488, 14010, 3176, 1758, 49521~
## $ new_cases <dbl> 19414, 547, 8, 451, 3829, 1028, 0, 2766, 687, 1199, 0, 29, ~
## $ new_deaths <dbl> 21, 11, 0, 5, 7, 8, 0, 3, 69, 34, 0, 0, 31, 2, 15, 7, 0, 1,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.65e+7 4.01e+7 1012359 41591
## 2 after 4.63e+7 3.99e+7 994497 39837
## 3 pctchg 4.78e-3 4.57e-3 0.0176 0.0422
##
##
## Processed for cdcHosp:
## Rows: 39,837
## Columns: 5
## $ date <date> 2020-10-18, 2020-10-17, 2020-10-13, 2020-10-12, 2020-10-08~
## $ state <chr> "VT", "VT", "NH", "ID", "ND", "ID", "NE", "MS", "DC", "HI",~
## $ inp <dbl> 2, 3, 34, 221, 218, 191, 316, 516, 156, 123, 198, 116, 102,~
## $ hosp_adult <dbl> 2, 3, 34, 219, 212, 189, 315, 462, 141, 122, 193, 109, 101,~
## $ hosp_ped <dbl> 0, 0, 0, 2, 6, 2, 6, 4, 15, 1, 5, 3, 1, 1, 0, 0, 1, 6, 32, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.28e+11 1.37e+11 1219443. 3.57e+10 1848256. 1.28e+11 1448866.
## 2 after 1.58e+11 6.64e+10 1022671. 1.73e+10 1645640 6.19e+10 1227708.
## 3 pctchg 5.19e- 1 5.16e- 1 0.161 5.16e- 1 0.110 5.17e- 1 0.153
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 24,888
## Columns: 9
## $ date <date> 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-1~
## $ state <chr> "NV", "SC", "NE", "ND", "CA", "MN", "DE", "WA", "AK", "CT"~
## $ vxa <dbl> 4846899, 7387622, 3169859, 1070327, 73947936, 10165098, 17~
## $ vxc <dbl> 1864022, 2914901, 1225968, 417012, 28314115, 3888695, 6696~
## $ vxcpoppct <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 69.0, 68.8, 72.3, 62.0, 78.8~
## $ vxcgte65 <dbl> 415049, 810448, 286617, 102377, 5246870, 882521, 180253, 1~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 95.0, 95.0, 93.8, 85.9, 95.0~
## $ vxcgte18 <dbl> 1694297, 2670106, 1088392, 380395, 24878761, 3415346, 6047~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 78.8, 78.5, 82.4, 73.0, 87.7~
##
## Integrated per capita data file:
## Rows: 41,727
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220416, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220416 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220416.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 439,873
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 8246
## 2 Critical Access Hospitals 117679
## 3 Long Term 30218
## 4 Short Term 283730
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 33
## 2 GU 176
## 3 MP 88
## 4 PR 4824
## 5 VI 176
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 27909 411160 804 439873
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 400911 35644 439873
## 3 icu_beds_used_7_day_avg 1649 385635 52589 439873
## 4 inpatient_beds_7_day_avg 1730 436407 1736 439873
## 5 staffed_icu_adult_patients_confirmed_an~ 4241 306239 129393 439873
## 6 total_adult_patients_hospitalized_confi~ 2362 304424 133087 439873
## 7 total_beds_7_day_avg 22106 417354 413 439873
## 8 total_icu_beds_7_day_avg 2064 415848 21961 439873## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220416, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220416 <- postProcessCDCDaily(cdc_daily_220416,
dataThruLabel="Mar 2022",
keyDatesBurden=c("2022-03-31", "2021-09-30",
"2021-03-31", "2020-09-30"
),
keyDatesVaccine=c("2022-03-31", "2021-11-30",
"2021-07-31", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220416 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220416,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
Peaks and valleys of key metrics are also plotted:
peakValleyCDCDaily(cdc_daily_220416)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,012 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 6,002 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220416 <- skinnyHHS(indivHosp_20220416) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220416,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to mid-April 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to mid-April 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220501.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220501.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220501.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220416")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220416")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220416")$dfRaw$vax
)
cdc_daily_220501 <- readRunCDCDaily(thruLabel="Apr 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-04-14 new_deaths 555 747 192 0.29493088
## 2 2022-04-10 new_deaths 64 49 15 0.26548673
## 3 2022-04-12 new_deaths 430 396 34 0.08232446
## 4 2022-04-13 new_deaths 596 641 45 0.07275667
## 5 2022-04-03 new_deaths 101 94 7 0.07179487
## 6 2022-04-11 new_deaths 324 303 21 0.06698565
## 7 2022-04-09 new_deaths 150 141 9 0.06185567
## 8 2022-04-02 new_deaths 162 154 8 0.05063291
## 9 2022-04-09 new_cases 14426 13247 1179 0.08520941
## 10 2022-04-13 new_cases 47095 51168 4073 0.08289997
## 11 2022-04-10 new_cases 18655 17702 953 0.05242457
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 5084815 5070287 14528 0.002861222
## 2 CO tot_cases 373235234 372250769 984465 0.002641136
## 3 KY new_deaths 15445 15251 194 0.012640083
## 4 NV new_deaths 10223 10340 117 0.011379663
## 5 AL new_deaths 19552 19502 50 0.002560557
## 6 CO new_deaths 12001 12031 30 0.002496671
## 7 FL new_deaths 73846 73689 157 0.002128309
## 8 SC new_deaths 17733 17698 35 0.001975671
## 9 NC new_deaths 23362 23334 28 0.001199246
## 10 CO new_cases 1373102 1361600 11502 0.008411885
## 11 NC new_cases 2643272 2639241 4031 0.001526168
## 12 KY new_cases 1323254 1321450 1804 0.001364236
##
##
##
## Raw file for cdcDaily:
## Rows: 49,740
## Columns: 15
## $ date <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state <chr> "KS", "AS", "AR", "MP", "AS", "CO", "MA", "PR", "GA", "~
## $ tot_cases <dbl> 621273, 11, 56199, 37, 0, 944337, 704796, 35112, 118710~
## $ conf_cases <dbl> 470516, NA, NA, 37, NA, 862950, 659246, 34791, 937515, ~
## $ prob_cases <dbl> 150757, NA, NA, 0, NA, 81387, 45550, 321, 249592, 94752~
## $ new_cases <dbl> 19414, 0, 547, 1, 0, 10817, 451, 619, 3829, 203, 0, 175~
## $ pnew_case <dbl> 6964, 0, 0, 0, 0, 931, 46, 1, 1144, 54, 0, 168, 317, 0,~
## $ tot_deaths <dbl> 7162, 0, 674, 2, 0, 10271, 17818, 805, 21690, 7256, 0, ~
## $ conf_death <dbl> NA, NA, NA, 2, NA, 9089, 17458, 624, 18725, 6176, 0, 28~
## $ prob_death <dbl> NA, NA, NA, 0, NA, 1182, 360, 181, 2965, 1080, 0, 5188,~
## $ new_deaths <dbl> 21, 0, 11, 0, 0, 31, 5, 3, 7, 0, 0, 20, 3, 0, 69, 34, 0~
## $ pnew_death <dbl> 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, -7, 0, 0, 0, 0, NA, 0,~
## $ created_at <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases <chr> "Agree", NA, "Not agree", "Agree", NA, "Agree", "Agree"~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Agree", "Agree", ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 4621 4270 351 0.07895625
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AS inp 354 356 2 0.005633803
## 2 VI inp 4288 4296 8 0.001863933
## 3 NH hosp_ped 898 944 46 0.049945711
## 4 ME hosp_ped 1889 1945 56 0.029212311
## 5 WV hosp_ped 5138 5034 104 0.020448289
## 6 AR hosp_ped 11326 11212 114 0.010116248
## 7 ID hosp_ped 3585 3550 35 0.009810792
## 8 AL hosp_ped 18802 18972 170 0.009000900
## 9 IN hosp_ped 16332 16230 102 0.006264971
## 10 NJ hosp_ped 16929 17023 94 0.005537229
## 11 MO hosp_ped 35405 35251 154 0.004359149
## 12 PR hosp_ped 17701 17774 73 0.004115574
## 13 CO hosp_ped 19247 19322 75 0.003889134
## 14 TN hosp_ped 20056 20133 77 0.003831894
## 15 NM hosp_ped 6922 6896 26 0.003763207
## 16 VA hosp_ped 15517 15465 52 0.003356788
## 17 UT hosp_ped 8779 8750 29 0.003308803
## 18 AK hosp_ped 2227 2233 6 0.002690583
## 19 MD hosp_ped 13808 13771 37 0.002683201
## 20 MS hosp_ped 9988 10011 23 0.002300115
## 21 KY hosp_ped 17263 17298 35 0.002025404
## 22 GA hosp_ped 46480 46386 94 0.002024422
## 23 CA hosp_ped 70500 70639 139 0.001969689
## 24 FL hosp_ped 85064 84922 142 0.001670726
## 25 IA hosp_ped 6846 6835 11 0.001608070
## 26 CT hosp_ped 6956 6966 10 0.001436575
## 27 SC hosp_ped 8214 8204 10 0.001218175
## 28 TX hosp_ped 103805 103689 116 0.001118105
## 29 NV hosp_ped 4552 4547 5 0.001099022
## 30 AS hosp_adult 350 352 2 0.005698006
## 31 VI hosp_adult 4039 4047 8 0.001978729
## 32 NH hosp_adult 92783 92686 97 0.001045997
##
##
##
## Raw file for cdcHosp:
## Rows: 42,401
## Columns: 135
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> ~
## $ all_pediatric_inpatient_beds <dbl> ~
## $ all_pediatric_inpatient_beds_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 32,472
## Columns: 82
## $ date <date> 2022-04-30, 2022-04-30, 2022-0~
## $ MMWR_week <dbl> 17, 17, 17, 17, 17, 17, 17, 17,~
## $ state <chr> "IN", "NM", "US", "ME", "KY", "~
## $ Distributed <dbl> 13462080, 4474445, 728344715, 3~
## $ Distributed_Janssen <dbl> 607200, 187600, 30749100, 15400~
## $ Distributed_Moderna <dbl> 4734100, 1748900, 270415980, 13~
## $ Distributed_Pfizer <dbl> 8120780, 2537945, 427179635, 19~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 199965, 213391, 219375, 253996,~
## $ Distributed_Per_100k_12Plus <dbl> 235968, 250328, 256893, 288138,~
## $ Distributed_Per_100k_18Plus <dbl> 260679, 276031, 282020, 311698,~
## $ Distributed_Per_100k_65Plus <dbl> 1239900, 1184950, 1329290, 1196~
## $ vxa <dbl> 9524604, 3935871, 575765730, 28~
## $ Administered_12Plus <dbl> 9276622, 3800236, 557160223, 27~
## $ Administered_18Plus <dbl> 8736719, 3533433, 520923954, 26~
## $ Administered_65Plus <dbl> 2742875, 1042375, 146082778, 87~
## $ Administered_Janssen <dbl> 305604, 119590, 18703265, 14369~
## $ Administered_Moderna <dbl> 3431369, 1598596, 216827616, 11~
## $ Administered_Pfizer <dbl> 5756128, 2208144, 339679887, 15~
## $ Administered_Unk_Manuf <dbl> 31503, 9541, 554962, 3615, 2531~
## $ Admin_Per_100k <dbl> 141478, 187706, 173419, 211476,~
## $ Admin_Per_100k_12Plus <dbl> 162604, 212609, 196515, 232502,~
## $ Admin_Per_100k_18Plus <dbl> 169177, 217980, 201705, 237494,~
## $ Admin_Per_100k_65Plus <dbl> 252627, 276048, 266613, 307600,~
## $ Recip_Administered <dbl> 9536900, 4083307, 575765730, 28~
## $ Administered_Dose1_Recip <dbl> 4135482, 1837743, 257641065, 12~
## $ Administered_Dose1_Pop_Pct <dbl> 61.4, 87.6, 77.6, 90.4, 66.1, 8~
## $ Administered_Dose1_Recip_12Plus <dbl> 3990122, 1755545, 247398872, 11~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 69.9, 95.0, 87.3, 95.0, 75.2, 9~
## $ Administered_Dose1_Recip_18Plus <dbl> 3731041, 1618184, 229910032, 11~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 72.2, 95.0, 89.0, 95.0, 77.6, 9~
## $ Administered_Dose1_Recip_65Plus <dbl> 1009378, 428113, 56761008, 3298~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 93.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 3690380, 1490068, 219675939, 10~
## $ vxcpoppct <dbl> 54.8, 71.1, 66.2, 79.5, 57.4, 6~
## $ Series_Complete_12Plus <dbl> 3588448, 1428092, 211424055, 10~
## $ Series_Complete_12PlusPop_Pct <dbl> 62.9, 79.9, 74.6, 86.7, 65.4, 7~
## $ vxcgte18 <dbl> 3366149, 1314314, 196498734, 96~
## $ vxcgte18pct <dbl> 65.2, 81.1, 76.1, 88.2, 67.5, 7~
## $ vxcgte65 <dbl> 943206, 356210, 49434951, 28625~
## $ vxcgte65pct <dbl> 86.9, 94.3, 90.2, 95.0, 86.3, 9~
## $ Series_Complete_Janssen <dbl> 281574, 109986, 16953131, 13183~
## $ Series_Complete_Moderna <dbl> 1254015, 563214, 76433248, 3852~
## $ Series_Complete_Pfizer <dbl> 2146277, 814554, 126132484, 551~
## $ Series_Complete_Unk_Manuf <dbl> 8514, 2314, 157076, 845, 1838, ~
## $ Series_Complete_Janssen_12Plus <dbl> 281541, 109967, 16948031, 13180~
## $ Series_Complete_Moderna_12Plus <dbl> 1253947, 563150, 76426770, 3852~
## $ Series_Complete_Pfizer_12Plus <dbl> 2044486, 752672, 117894600, 510~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 8474, 2303, 154654, 837, 1819, ~
## $ Series_Complete_Janssen_18Plus <dbl> 281258, 109824, 16921288, 13174~
## $ Series_Complete_Moderna_18Plus <dbl> 1253587, 562729, 76344491, 3851~
## $ Series_Complete_Pfizer_18Plus <dbl> 1822963, 639497, 103084542, 448~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 8341, 2264, 148413, 755, 1713, ~
## $ Series_Complete_Janssen_65Plus <dbl> 31120, 21383, 2360115, 24650, 3~
## $ Series_Complete_Moderna_65Plus <dbl> 461227, 167308, 23565506, 13124~
## $ Series_Complete_Pfizer_65Plus <dbl> 446929, 166389, 23443158, 13006~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 3930, 1130, 66172, 294, 846, 36~
## $ Additional_Doses <dbl> 1693251, 739576, 100600067, 597~
## $ Additional_Doses_Vax_Pct <dbl> 45.9, 49.6, 45.8, 55.9, 44.2, 4~
## $ Additional_Doses_12Plus <dbl> 1689014, 739365, 100571760, 597~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 47.1, 51.8, 47.6, 58.1, 45.5, 4~
## $ Additional_Doses_18Plus <dbl> 1636009, 706410, 96911167, 5754~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 48.6, 53.7, 49.3, 59.6, 47.2, 4~
## $ Additional_Doses_50Plus <dbl> 1114515, 442723, 61293070, 3915~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 59.7, 63.5, 60.7, 70.1, 59.1, 6~
## $ Additional_Doses_65Plus <dbl> 652191, 246351, 33899976, 22276~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 69.1, 69.2, 68.6, 77.8, 68.5, 6~
## $ Additional_Doses_Moderna <dbl> 659070, 310369, 43251133, 27256~
## $ Additional_Doses_Pfizer <dbl> 1011148, 418139, 55814930, 3131~
## $ Additional_Doses_Janssen <dbl> 21456, 10800, 1502478, 10855, 2~
## $ Additional_Doses_Unk_Manuf <dbl> 1577, 268, 31526, 622, 220, 59,~
## $ Administered_Dose1_Recip_5Plus <dbl> 4135027, 1837487, 257532058, 12~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 65.5, 93.0, 82.5, 94.9, 70.4, 8~
## $ Series_Complete_5Plus <dbl> 3690294, 1490006, 219626527, 10~
## $ Series_Complete_5PlusPop_Pct <dbl> 58.4, 75.4, 70.3, 83.5, 61.1, 7~
## $ Administered_5Plus <dbl> 9524148, 3935594, 575608906, 28~
## $ Admin_Per_100k_5Plus <dbl> 150845, 199186, 184333, 221953,~
## $ Distributed_Per_100k_5Plus <dbl> 213214, 226458, 233246, 266598,~
## $ Series_Complete_Moderna_5Plus <dbl> 1254006, 563197, 76430070, 3852~
## $ Series_Complete_Pfizer_5Plus <dbl> 2146218, 814520, 126089538, 551~
## $ Series_Complete_Janssen_5Plus <dbl> 281557, 109976, 16950044, 13181~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 8513, 2313, 156875, 845, 1837, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.40e+10 3.85e+8 8.07e+7 974312 48911
## 2 after 2.39e+10 3.83e+8 8.00e+7 969594 42279
## 3 pctchg 5.35e- 3 4.40e-3 7.66e-3 0.00484 0.136
##
##
## Processed for cdcDaily:
## Rows: 42,279
## Columns: 6
## $ date <date> 2022-01-14, 2020-08-22, 2021-12-31, 2021-05-22, 2021-08-01~
## $ state <chr> "KS", "AR", "CO", "MA", "GA", "OK", "GA", "GA", "TX", "AK",~
## $ tot_cases <dbl> 621273, 56199, 944337, 704796, 1187107, 449170, 147804, 383~
## $ tot_deaths <dbl> 7162, 674, 10271, 17818, 21690, 7256, 3176, 1758, 49521, 9,~
## $ new_cases <dbl> 19414, 547, 10817, 451, 3829, 203, 2766, 687, 1199, 7, 1723~
## $ new_deaths <dbl> 21, 11, 31, 5, 7, 0, 3, 69, 34, 0, 127, 31, 0, 9, 15, 7, 0,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.68e+7 4.03e+7 1027403 42401
## 2 after 4.66e+7 4.01e+7 1008991 40602
## 3 pctchg 4.82e-3 4.60e-3 0.0179 0.0424
##
##
## Processed for cdcHosp:
## Rows: 40,602
## Columns: 5
## $ date <date> 2020-10-13, 2020-10-12, 2020-10-09, 2020-10-04, 2020-09-22~
## $ state <chr> "RI", "VT", "RI", "RI", "VT", "RI", "AK", "RI", "DE", "VT",~
## $ inp <dbl> 124, 0, 116, 92, 1, 85, 50, 75, 89, 6, 12, 160, 69, 33, 70,~
## $ hosp_adult <dbl> 123, 0, 115, 91, 1, 84, 49, 75, 84, 5, 9, 115, 66, 33, 55, ~
## $ hosp_ped <dbl> 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 3, 46, 0, 0, 15, 147, 0, 1, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.45e+11 1.44e+11 1278961. 3.72e+10 1927715. 1.34e+11 1516669.
## 2 after 1.66e+11 6.96e+10 1072129. 1.80e+10 1714305. 6.48e+10 1284690.
## 3 pctchg 5.19e- 1 5.16e- 1 0.162 5.16e- 1 0.111 5.16e- 1 0.153
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 25,653
## Columns: 9
## $ date <date> 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-3~
## $ state <chr> "IN", "NM", "ME", "KY", "DE", "MA", "CA", "TX", "WA", "SC"~
## $ vxa <dbl> 9524604, 3935871, 2842688, 6522218, 1813671, 14845867, 750~
## $ vxc <dbl> 3690380, 1490068, 1069169, 2563070, 672952, 5441750, 28464~
## $ vxcpoppct <dbl> 54.8, 71.1, 79.5, 57.4, 69.1, 79.0, 72.0, 61.4, 72.6, 56.7~
## $ vxcgte65 <dbl> 943206, 356210, 286250, 647476, 181583, 1124456, 5280949, ~
## $ vxcgte65pct <dbl> 86.9, 94.3, 95.0, 86.3, 95.0, 95.0, 90.5, 87.3, 94.2, 86.6~
## $ vxcgte18 <dbl> 3366149, 1314314, 965876, 2338367, 607641, 4805717, 249983~
## $ vxcgte18pct <dbl> 65.2, 81.1, 88.2, 67.5, 78.9, 86.8, 81.6, 72.5, 82.6, 66.2~
##
## Integrated per capita data file:
## Rows: 42,492
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220501, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220501 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220501.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 449,805
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 8432
## 2 Critical Access Hospitals 120273
## 3 Long Term 30899
## 4 Short Term 290201
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 35
## 2 GU 180
## 3 MP 90
## 4 PR 4930
## 5 VI 180
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 32098 416881 826 449805
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 409902 36585 449805
## 3 icu_beds_used_7_day_avg 1650 394165 53990 449805
## 4 inpatient_beds_7_day_avg 1728 446299 1778 449805
## 5 staffed_icu_adult_patients_confirmed_an~ 4238 313313 132254 449805
## 6 total_adult_patients_hospitalized_confi~ 2359 310541 136905 449805
## 7 total_beds_7_day_avg 26089 423289 427 449805
## 8 total_icu_beds_7_day_avg 2065 425219 22521 449805## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220501, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501,
dataThruLabel="Apr 2022",
keyDatesBurden=c("2022-04-29", "2021-10-31",
"2021-04-30", "2020-10-31"
),
keyDatesVaccine=c("2022-04-29", "2021-12-31",
"2021-08-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220501,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
The post-process function is working incorrectly on the pediatric data. It appears that a few category labels changed syntax. Function createBurdenPivot() is updated to a better formed grep sequence for the latest data:
burdenPivotList_220501$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n())## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can
## override using the `.groups` argument.## # A tibble: 19 × 6
## # Groups: adultPed, confSusp, age [18]
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.20e4 42401
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.49e5 42401
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 3.65e5 42401
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.55e5 42401
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.25e5 42401
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 9.29e5 42401
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 9.05e5 42401
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 7.73e5 42401
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.35e4 42401
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.27e5 42401
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 2.97e5 42401
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.03e5 42401
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 4.79e5 42401
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 6.54e5 42401
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 6.32e5 42401
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 5.75e5 42401
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.30e5 42401
## 18 ped suspected 0-19 all_pediatric_inpatient_bed_occupied 2.00e7 42401
## 19 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.19e5 42401# Create pivoted burden data
createBurdenPivot <- function(lst,
dataThru,
minDatePlot="2020-08-01",
plotByState=c(state.abb, "DC")
) {
# FUNCTION ARGUMENTS:
# lst: a processed list that includes sub-component $dfRaw$cdcHosp
# dataThru: character string to be used for 'data through'; most commonly MMM-YY
# minDatePlot: starting date for plots
# plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
# Convert minDatePlot to Date if passed as character
if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
# Create the hospitalized by age data
hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
select(state,
date,
grep(x=names(.), pattern="previous_.*ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="previous_.*pediatric.*[tm]ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped",
"0-17",
stringr::str_replace_all(string=name, pattern=".*_", replacement="")
),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
# Create the pivoted burden data
dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
# Plot for overall trends by age group
p1 <- hospAge %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"),
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Create three main plots of hospitalized by age data
print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
# Plot for trends by state and age group
if (!isFALSE(plotByState)) {
p2 <- hospAge %>%
filter(state %in% plotByState, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")
print(p2)
}
# Return key data (do not return plot objects)
list(hospAge=hospAge, dfPivot=dfPivot)
}
# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501,
dataThruLabel="Apr 2022",
keyDatesBurden=c("2022-04-29", "2021-10-31",
"2021-04-30", "2020-10-31"
),
keyDatesVaccine=c("2022-04-29", "2021-12-31",
"2021-08-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220501,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220501$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n())## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can
## override using the `.groups` argument.## # A tibble: 18 × 6
## # Groups: adultPed, confSusp, age [18]
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 41964 42401
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 249178 42401
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 365345 42401
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 454857 42401
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 724912 42401
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 929208 42401
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 905339 42401
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 772981 42401
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 33526 42401
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 226590 42401
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 296759 42401
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 303443 42401
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 479067 42401
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 653801 42401
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 632370 42401
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 574885 42401
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 130296 42401
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 318942 42401Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220501)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,192 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 6,182 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220501 <- skinnyHHS(indivHosp_20220501) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220501,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to April 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
createGeoMap(modStateHosp_20220501 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to April 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
A function is created for hospital post-processing:
hospitalCapacityCDCDaily <- function(df,
createData=TRUE,
returnData=createData,
maxCapacity=1.2,
plotSub="start to finish"
) {
# FUNCTION ARGUMENTS:
# df: the key data frame
# createData: boolean, if TRUE then convert df for use in processing
# if FALSE, use df as-is
# returnData: boolean, should df be returned (defaults to TRUE is modified, FALSE otherwise)
# maxCapacity: states that exceed this capacity level will not be plotted (explore separately)
# plotSub: subtitle to use for plots
# Convert data if requested
if(isTRUE(createData)) df <- skinnyHHS(df) %>% imputeNACapacity() %>% sumImputedHHS()
# Create ICU summary
createGeoMap(df,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle=plotSub,
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Get list of states that may complicate map
pctState <- df %>%
mutate(pctAdult=adult_beds_occupied/adult_beds, pctCovidAdult=adult_beds_covid/adult_beds)
exclStates <- pctState %>% filter(pctAdult > maxCapacity) %>% count(state) %>% pull(state)
if(length(exclStates) > 0) plotSub <- paste0(plotSub, "\n(", paste(exclStates, collapse=", "), " data excluded)")
# Create the adult beds summary
createGeoMap(df %>% filter(!(state %in% all_of(exclStates))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle=plotSub,
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
# Return the data if requested (defaults to only if createData is TRUE)
if(isTRUE(returnData)) return(df)
}
hospitalCapacityCDCDaily(modStateHosp_20220501, createData=FALSE, plotSub="August 2020 to April 2022")
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220602.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220602.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220602.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220501")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220501")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220501")$dfRaw$vax
)
cdc_daily_220602 <- readRunCDCDaily(thruLabel="May 31, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-04-24 new_deaths 56 40 16 0.33333333
## 2 2022-04-16 new_deaths 40 31 9 0.25352113
## 3 2021-07-30 new_deaths 522 651 129 0.21994885
## 4 2020-06-22 new_deaths 431 496 65 0.14023732
## 5 2022-04-02 new_deaths 143 162 19 0.12459016
## 6 2022-03-20 new_deaths 147 134 13 0.09252669
## 7 2022-04-03 new_deaths 110 101 9 0.08530806
## 8 2022-03-12 new_deaths 505 548 43 0.08167142
## 9 2022-03-05 new_deaths 322 346 24 0.07185629
## 10 2022-04-25 new_deaths 195 182 13 0.06896552
## 11 2022-03-27 new_deaths 91 85 6 0.06818182
## 12 2022-04-26 new_deaths 355 334 21 0.06095791
## 13 2022-01-02 new_deaths 380 358 22 0.05962060
## 14 2022-04-28 new_deaths 282 266 16 0.05839416
## 15 2022-04-29 new_deaths 476 503 27 0.05515832
## 16 2022-04-27 new_deaths 727 768 41 0.05484950
## 17 2022-03-19 new_deaths 196 207 11 0.05459057
## 18 2022-03-06 new_deaths 364 345 19 0.05359661
## 19 2022-04-27 new_cases 79917 88535 8618 0.10231995
## 20 2022-04-29 new_cases 86370 78910 7460 0.09027106
## 21 2022-04-23 new_cases 27418 25206 2212 0.08406811
## 22 2022-04-24 new_cases 25174 23358 1816 0.07483722
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 DE tot_deaths 1172100 1092040 80060 0.070720008
## 2 NC tot_deaths 8864991 8762468 102523 0.011632193
## 3 KY tot_deaths 5355366 5316652 38714 0.007255235
## 4 DE tot_cases 75721383 76164588 443205 0.005836023
## 5 CO tot_cases 394838272 393905105 933167 0.002366212
## 6 NC new_deaths 24600 23405 1195 0.049786481
## 7 KY new_deaths 15904 15523 381 0.024246667
## 8 DE new_deaths 2930 2907 23 0.007880761
## 9 AL new_deaths 19627 19570 57 0.002908386
## 10 FL new_deaths 74141 73948 193 0.002606541
## 11 CO new_cases 1391889 1382905 8984 0.006475436
## 12 DE new_cases 262456 263913 1457 0.005536040
## 13 NC new_cases 2669969 2659255 10714 0.004020848
## 14 SC new_cases 1477235 1474272 2963 0.002007788
##
##
##
## Raw file for cdcDaily:
## Rows: 51,660
## Columns: 15
## $ date <date> 2021-03-11, 2021-02-12, 2021-08-25, 2022-05-30, 2020-0~
## $ state <chr> "KS", "UT", "CO", "AK", "TX", "AS", "CO", "MA", "PR", "~
## $ tot_cases <dbl> 297229, 359641, 608176, 251425, 361125, 0, 58307, 70479~
## $ conf_cases <dbl> 241035, 359641, 562668, NA, NA, NA, 53980, 659246, 3479~
## $ prob_cases <dbl> 56194, 0, 45508, NA, NA, NA, 4327, 45550, 321, 249592, ~
## $ new_cases <dbl> 0, 1060, 1974, 0, 9507, 0, 223, 451, 619, 3829, 0, 0, 0~
## $ pnew_case <dbl> 0, 0, 215, 0, 0, 0, 11, 46, 1, 1144, 0, 0, 0, 317, 5246~
## $ tot_deaths <dbl> 4851, 1785, 7088, 1252, 7981, 0, 1944, 17818, 805, 2169~
## $ conf_death <dbl> NA, 1729, 6282, NA, NA, NA, 1596, 17458, 624, 18725, NA~
## $ prob_death <dbl> NA, 56, 806, NA, NA, NA, 348, 360, 181, 2965, NA, NA, 4~
## $ new_deaths <dbl> 0, 11, 4, 0, 281, 0, 0, 5, 3, 7, 0, 0, 0, 3, 417, 7, 0,~
## $ pnew_death <dbl> 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, NA, 0, NA~
## $ created_at <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "08~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "N/A", "Not agree", NA, "Agr~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "N/A", "Not agree", NA, "Agree~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 4737 4158 579 0.1301855
## 2 2022-04-30 hosp_ped 930 1042 112 0.1135903
## 3 2020-07-25 hosp_ped 4159 4621 462 0.1052392
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VI inp 4362 4352 10 0.002295157
## 2 NH hosp_ped 876 922 46 0.051167964
## 3 WV hosp_ped 5009 5185 176 0.034530116
## 4 ME hosp_ped 1945 1995 50 0.025380711
## 5 NV hosp_ped 4724 4630 94 0.020098354
## 6 MA hosp_ped 10782 10657 125 0.011660992
## 7 ID hosp_ped 3579 3615 36 0.010008340
## 8 TN hosp_ped 20271 20100 171 0.008471428
## 9 DE hosp_ped 4471 4504 33 0.007353760
## 10 SD hosp_ped 4050 4077 27 0.006644518
## 11 NJ hosp_ped 17232 17122 110 0.006403912
## 12 AL hosp_ped 18848 18955 107 0.005660926
## 13 PR hosp_ped 18423 18324 99 0.005388195
## 14 NM hosp_ped 7040 7003 37 0.005269529
## 15 IN hosp_ped 16341 16423 82 0.005005494
## 16 KS hosp_ped 4449 4427 22 0.004957188
## 17 AR hosp_ped 11376 11432 56 0.004910558
## 18 MS hosp_ped 10152 10107 45 0.004442470
## 19 KY hosp_ped 17644 17582 62 0.003520127
## 20 UT hosp_ped 8889 8919 30 0.003369272
## 21 PA hosp_ped 48788 48625 163 0.003346576
## 22 SC hosp_ped 8291 8268 23 0.002777946
## 23 NC hosp_ped 27671 27724 53 0.001913530
## 24 FL hosp_ped 85396 85542 146 0.001708222
## 25 MO hosp_ped 35728 35789 61 0.001705888
## 26 OK hosp_ped 23815 23775 40 0.001681025
## 27 OR hosp_ped 9286 9301 15 0.001614031
## 28 WA hosp_ped 12700 12720 20 0.001573564
## 29 AZ hosp_ped 25363 25324 39 0.001538856
## 30 HI hosp_ped 2447 2450 3 0.001225240
## 31 CA hosp_ped 71305 71223 82 0.001150651
## 32 TX hosp_ped 105183 105302 119 0.001130722
## 33 VI hosp_adult 4113 4103 10 0.002434275
##
##
##
## Raw file for cdcHosp:
## Rows: 44,129
## Columns: 135
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> ~
## $ all_pediatric_inpatient_beds <dbl> ~
## $ all_pediatric_inpatient_beds_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Second_Booster
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 34,520
## Columns: 83
## $ date <date> 2022-06-01, 2022-06-01, 2022-0~
## $ MMWR_week <dbl> 22, 22, 22, 22, 22, 22, 22, 22,~
## $ state <chr> "MP", "TN", "HI", "PA", "FL", "~
## $ Distributed <dbl> 127130, 13267730, 3582480, 3162~
## $ Distributed_Janssen <dbl> 3600, 517400, 124600, 1537400, ~
## $ Distributed_Moderna <dbl> 25720, 5085840, 1344520, 121049~
## $ Distributed_Pfizer <dbl> 97810, 7664490, 2113360, 179791~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 245183, 194280, 253023, 247005,~
## $ Distributed_Per_100k_12Plus <dbl> 300615, 227360, 295802, 285353,~
## $ Distributed_Per_100k_18Plus <dbl> 352405, 249435, 321010, 311010,~
## $ Distributed_Per_100k_65Plus <dbl> 3386520, 1160380, 1334520, 1321~
## $ vxa <dbl> 109753, 10128911, 2985746, 2350~
## $ Administered_12Plus <dbl> 101165, 9920484, 2874468, 22760~
## $ Administered_18Plus <dbl> 89008, 9438725, 2681590, 214476~
## $ Administered_65Plus <dbl> 8877, 3018351, 783871, 6852918,~
## $ Administered_Janssen <dbl> 1381, 267298, 71494, 783616, 14~
## $ Administered_Moderna <dbl> 15143, 3880829, 1097497, 909454~
## $ Administered_Pfizer <dbl> 93220, 5914777, 1816453, 136239~
## $ Administered_Unk_Manuf <dbl> 9, 66007, 302, 1452, 145805, 2,~
## $ Admin_Per_100k <dbl> 211670, 148318, 210877, 183593,~
## $ Admin_Per_100k_12Plus <dbl> 239217, 170000, 237342, 205388,~
## $ Admin_Per_100k_18Plus <dbl> 246730, 177449, 240285, 210946,~
## $ Admin_Per_100k_65Plus <dbl> 236468, 263982, 292001, 286330,~
## $ Recip_Administered <dbl> 109921, 9944936, 2998151, 23563~
## $ Administered_Dose1_Recip <dbl> 45593, 4265547, 1243424, 109511~
## $ Administered_Dose1_Pop_Pct <dbl> 87.9, 62.5, 87.8, 85.5, 79.7, 4~
## $ Administered_Dose1_Recip_12Plus <dbl> 41088, 4153960, 1183344, 105535~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 71.2, 95.0, 95.0, 89.3, 5~
## $ Administered_Dose1_Recip_18Plus <dbl> 35684, 3918229, 1094955, 991214~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 73.7, 95.0, 95.0, 91.2, 5~
## $ Administered_Dose1_Recip_65Plus <dbl> 3247, 1059579, 272827, 2752789,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 86.5, 92.7, 95.0, 95.0, 95.0, 4~
## $ vxc <dbl> 43379, 3742228, 1113175, 884697~
## $ vxcpoppct <dbl> 83.7, 54.8, 78.6, 69.1, 67.3, 3~
## $ Series_Complete_12Plus <dbl> 39312, 3649584, 1061296, 852234~
## $ Series_Complete_12PlusPop_Pct <dbl> 93.0, 62.5, 87.6, 76.9, 75.6, 4~
## $ vxcgte18 <dbl> 34156, 3448630, 981204, 7997796~
## $ vxcgte18pct <dbl> 94.7, 64.8, 87.9, 78.7, 77.3, 4~
## $ vxcgte65 <dbl> 3124, 971113, 249781, 2297628, ~
## $ vxcgte65pct <dbl> 83.2, 84.9, 93.0, 95.0, 91.6, 3~
## $ Series_Complete_Janssen <dbl> 1169, 237079, 65595, 727556, 13~
## $ Series_Complete_Moderna <dbl> 5225, 1328160, 367035, 3165750,~
## $ Series_Complete_Pfizer <dbl> 36982, 2163982, 680525, 4952969~
## $ Series_Complete_Unk_Manuf <dbl> 3, 13007, 20, 695, 41446, 3, 53~
## $ Series_Complete_Janssen_12Plus <dbl> 1169, 237021, 65565, 727452, 13~
## $ Series_Complete_Moderna_12Plus <dbl> 5225, 1328098, 366976, 3165432,~
## $ Series_Complete_Pfizer_12Plus <dbl> 32915, 2071553, 628735, 4628770~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 3, 12912, 20, 692, 40906, 3, 53~
## $ Series_Complete_Janssen_18Plus <dbl> 1169, 236756, 65381, 726950, 13~
## $ Series_Complete_Moderna_18Plus <dbl> 5224, 1327470, 366140, 3163173,~
## $ Series_Complete_Pfizer_18Plus <dbl> 27760, 1871626, 549664, 4107032~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3, 12778, 19, 641, 40177, 3, 52~
## $ Series_Complete_Janssen_65Plus <dbl> 120, 36069, 11781, 91482, 21518~
## $ Series_Complete_Moderna_65Plus <dbl> 503, 482492, 109858, 1080575, 1~
## $ Series_Complete_Pfizer_65Plus <dbl> 2501, 446024, 128135, 1125265, ~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 0, 6528, 7, 306, 22063, 0, 248,~
## $ Additional_Doses <dbl> 21262, 1668987, 579418, 3823362~
## $ Additional_Doses_Vax_Pct <dbl> 49.0, 44.6, 52.1, 43.2, 40.8, 3~
## $ Additional_Doses_12Plus <dbl> 21249, 1666974, 577890, 3816418~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 54.1, 45.7, 54.5, 44.8, 41.8, 3~
## $ Additional_Doses_18Plus <dbl> 19639, 1629881, 552365, 3685510~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 57.5, 47.3, 56.3, 46.1, 43.3, 3~
## $ Additional_Doses_50Plus <dbl> 9565, 1136940, 355796, 2539831,~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 70.3, 59.5, 71.7, 56.7, 55.1, 4~
## $ Additional_Doses_65Plus <dbl> 2374, 672253, 199142, 1480123, ~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 76.0, 69.2, 79.7, 64.4, 63.1, 5~
## $ Additional_Doses_Moderna <dbl> 4188, 704060, 257346, 1669451, ~
## $ Additional_Doses_Pfizer <dbl> 16861, 937786, 315808, 2092068,~
## $ Additional_Doses_Janssen <dbl> 213, 22577, 6260, 61759, 106718~
## $ Additional_Doses_Unk_Manuf <dbl> 0, 4564, 4, 84, 8319, 0, 395, 2~
## $ Administered_Dose1_Recip_5Plus <dbl> 45587, 4264774, 1241337, 109476~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 94.8, 66.4, 93.3, 90.4, 84.2, 5~
## $ Series_Complete_5Plus <dbl> 43379, 3742071, 1112304, 884561~
## $ Series_Complete_5PlusPop_Pct <dbl> 90.2, 58.3, 83.6, 73.1, 71.1, 4~
## $ Administered_5Plus <dbl> 109747, 10127951, 2983465, 2349~
## $ Admin_Per_100k_5Plus <dbl> 228155, 157742, 224211, 194139,~
## $ Distributed_Per_100k_5Plus <dbl> 264293, 206644, 269227, 261247,~
## $ Series_Complete_Moderna_5Plus <dbl> 5225, 1328142, 366985, 3165623,~
## $ Series_Complete_Pfizer_5Plus <dbl> 36982, 2163886, 679732, 4951801~
## $ Series_Complete_Janssen_5Plus <dbl> 1169, 237039, 65567, 727500, 13~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 3, 13004, 20, 694, 41363, 3, 53~
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.66e+10 4.17e+8 8.36e+7 985417 50799
## 2 after 2.65e+10 4.15e+8 8.29e+7 980521 43911
## 3 pctchg 5.65e- 3 4.43e-3 9.10e-3 0.00497 0.136
##
##
## Processed for cdcDaily:
## Rows: 43,911
## Columns: 6
## $ date <date> 2021-03-11, 2021-02-12, 2021-08-25, 2022-05-30, 2020-07-23~
## $ state <chr> "KS", "UT", "CO", "AK", "TX", "CO", "MA", "GA", "TX", "OK",~
## $ tot_cases <dbl> 297229, 359641, 608176, 251425, 361125, 58307, 704796, 1187~
## $ tot_deaths <dbl> 4851, 1785, 7088, 1252, 7981, 1944, 17818, 21690, 0, 14010,~
## $ new_cases <dbl> 0, 1060, 1974, 0, 9507, 223, 451, 3829, 0, 0, 2766, 18997, ~
## $ new_deaths <dbl> 0, 11, 4, 0, 281, 0, 5, 7, 0, 0, 3, 417, 7, 0, 0, 9, 15, 31~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.75e+7 4.10e+7 1066764 44129
## 2 after 4.73e+7 4.08e+7 1046530 42234
## 3 pctchg 4.99e-3 4.76e-3 0.0190 0.0429
##
##
## Processed for cdcHosp:
## Rows: 42,234
## Columns: 5
## $ date <date> 2020-10-13, 2020-10-09, 2020-10-04, 2020-09-30, 2020-09-22~
## $ state <chr> "RI", "VT", "UT", "RI", "VT", "VT", "RI", "AK", "RI", "DE",~
## $ inp <dbl> 124, 0, 179, 91, 1, 4, 78, 50, 74, 89, 42, 69, 33, 8, 44, 6~
## $ hosp_adult <dbl> 123, 0, 160, 90, 1, 4, 78, 49, 74, 84, 42, 66, 33, 7, 42, 5~
## $ hosp_ped <dbl> 1, 0, 6, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.83e+11 1.58e+11 1406862. 4.05e+10 2098178. 1.47e+11 1662208.
## 2 after 1.84e+11 7.65e+10 1178390. 1.96e+10 1861606. 7.10e+10 1407029.
## 3 pctchg 5.18e- 1 5.16e- 1 0.162 5.16e- 1 0.113 5.16e- 1 0.154
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 27,285
## Columns: 9
## $ date <date> 2022-06-01, 2022-06-01, 2022-06-01, 2022-06-01, 2022-06-0~
## $ state <chr> "TN", "HI", "PA", "FL", "MT", "NE", "GA", "AK", "UT", "MI"~
## $ vxa <dbl> 10128911, 2985746, 23503533, 38132367, 1615900, 3251394, 1~
## $ vxc <dbl> 3742228, 1113175, 8846970, 14462011, 609664, 1234466, 5850~
## $ vxcpoppct <dbl> 54.8, 78.6, 69.1, 67.3, 57.0, 63.8, 55.1, 62.7, 64.6, 60.5~
## $ vxcgte65 <dbl> 971113, 249781, 2297628, 4119946, 178534, 288998, 1273888,~
## $ vxcgte65pct <dbl> 84.9, 93.0, 95.0, 91.6, 86.5, 92.5, 84.0, 86.6, 94.2, 88.8~
## $ vxcgte18 <dbl> 3448630, 981204, 7997796, 13325670, 556584, 1094822, 53117~
## $ vxcgte18pct <dbl> 64.8, 87.9, 78.7, 77.3, 66.2, 75.1, 65.5, 73.7, 77.9, 69.7~
##
## Integrated per capita data file:
## Rows: 44,124
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220602, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220602 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220602.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 474,854
## Columns: 128
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 8897
## 2 Critical Access Hospitals 127066
## 3 Long Term 32599
## 4 Short Term 306292
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 40
## 2 GU 190
## 3 MP 91
## 4 PR 5195
## 5 VI 190
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 42502 431481 871 474854
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 432608 38928 474854
## 3 icu_beds_used_7_day_avg 1651 415692 57511 474854
## 4 inpatient_beds_7_day_avg 1732 471229 1893 474854
## 5 staffed_icu_adult_patients_confirmed_an~ 4241 330351 140262 474854
## 6 total_adult_patients_hospitalized_confi~ 2362 326205 146287 474854
## 7 total_beds_7_day_avg 36207 438185 462 474854
## 8 total_icu_beds_7_day_avg 2066 448909 23879 474854## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220602, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220602 <- postProcessCDCDaily(cdc_daily_220602,
dataThruLabel="May 2022",
keyDatesBurden=c("2022-05-31", "2021-11-30",
"2021-05-31", "2020-11-30"
),
keyDatesVaccine=c("2022-05-31", "2022-01-31",
"2021-09-30", "2021-05-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220602 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220602,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220602$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n())## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.## # A tibble: 18 x 6
## # Groups: adultPed, confSusp, age [18]
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con~ 42727 44129
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con~ 255350 44129
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con~ 373410 44129
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con~ 461672 44129
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con~ 735828 44129
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con~ 946224 44129
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con~ 927067 44129
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con~ 801033 44129
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus~ 34599 44129
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus~ 233691 44129
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus~ 306167 44129
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus~ 312231 44129
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus~ 492966 44129
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus~ 674311 44129
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus~ 653272 44129
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus~ 594900 44129
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid~ 136757 44129
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid~ 332062 44129Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220602)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,576 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 6,566 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity plots are also updated:
modStateHosp_20220602 <- hospitalCapacityCDCDaily(indivHosp_20220602, plotSub="August 2020 to May 2022")
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220704.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220704.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220704.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220602")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220602")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220602")$dfRaw$vax
)
cdc_daily_220704 <- readRunCDCDaily(thruLabel="Jul 2, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-04 tot_cases 19 11 8 0.53333333
## 2 2020-02-05 tot_cases 19 11 8 0.53333333
## 3 2020-02-06 tot_cases 19 11 8 0.53333333
## 4 2020-02-14 tot_cases 24 14 10 0.52631579
## 5 2020-02-15 tot_cases 24 14 10 0.52631579
## 6 2020-02-16 tot_cases 24 14 10 0.52631579
## 7 2020-02-12 tot_cases 22 13 9 0.51428571
## 8 2020-02-07 tot_cases 20 12 8 0.50000000
## 9 2020-02-08 tot_cases 20 12 8 0.50000000
## 10 2020-02-09 tot_cases 20 12 8 0.50000000
## 11 2020-02-10 tot_cases 20 12 8 0.50000000
## 12 2020-02-11 tot_cases 20 12 8 0.50000000
## 13 2020-02-13 tot_cases 23 14 9 0.48648649
## 14 2020-02-17 tot_cases 26 16 10 0.47619048
## 15 2020-02-03 tot_cases 17 11 6 0.42857143
## 16 2020-02-18 tot_cases 31 21 10 0.38461538
## 17 2020-02-19 tot_cases 34 24 10 0.34482759
## 18 2020-02-20 tot_cases 35 25 10 0.33333333
## 19 2020-02-22 tot_cases 48 36 12 0.28571429
## 20 2020-02-23 tot_cases 48 36 12 0.28571429
## 21 2020-02-21 tot_cases 40 30 10 0.28571429
## 22 2020-02-24 tot_cases 52 40 12 0.26086957
## 23 2020-02-25 tot_cases 56 44 12 0.24000000
## 24 2020-02-26 tot_cases 64 52 12 0.20689655
## 25 2020-02-27 tot_cases 69 57 12 0.19047619
## 26 2020-02-28 tot_cases 73 61 12 0.17910448
## 27 2020-02-29 tot_cases 82 70 12 0.15789474
## 28 2020-03-01 tot_cases 100 88 12 0.12765957
## 29 2020-03-02 tot_cases 135 124 11 0.08494208
## 30 2020-03-03 tot_cases 186 175 11 0.06094183
## 31 2022-05-29 new_deaths 52 21 31 0.84931507
## 32 2022-05-30 new_deaths 76 32 44 0.81481481
## 33 2022-05-28 new_deaths 93 48 45 0.63829787
## 34 2022-05-22 new_deaths 70 53 17 0.27642276
## 35 2022-05-21 new_deaths 101 81 20 0.21978022
## 36 2022-04-16 new_deaths 47 40 7 0.16091954
## 37 2022-05-15 new_deaths 63 55 8 0.13559322
## 38 2022-05-31 new_deaths 367 330 37 0.10616930
## 39 2022-04-24 new_deaths 62 56 6 0.10169492
## 40 2022-05-26 new_deaths 322 295 27 0.08752026
## 41 2022-05-23 new_deaths 235 216 19 0.08425721
## 42 2022-04-03 new_deaths 119 110 9 0.07860262
## 43 2022-04-30 new_deaths 97 90 7 0.07486631
## 44 2022-05-28 new_cases 41154 30004 11150 0.31338711
## 45 2022-05-29 new_cases 39447 30180 9267 0.26618984
## 46 2022-05-31 new_cases 158170 183383 25213 0.14763741
## 47 2022-05-30 new_cases 56512 50736 5776 0.10771296
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 DE tot_deaths 1270880 1266093 4787 0.003773789
## 2 NC tot_deaths 9687566 9653360 34206 0.003537163
## 3 KY tot_deaths 5882901 5864807 18094 0.003080431
## 4 FSM tot_cases 2790 2927 137 0.047927235
## 5 CO tot_cases 444926166 440093591 4832575 0.010920830
## 6 KY tot_cases 407328006 406058926 1269080 0.003120483
## 7 NC new_deaths 25091 24660 431 0.017326285
## 8 KY new_deaths 16093 15957 136 0.008486739
## 9 DE new_deaths 2973 2956 17 0.005734525
## 10 FL new_deaths 74905 74557 348 0.004656702
## 11 AL new_deaths 19705 19664 41 0.002082857
## 12 FSM new_cases 24 26 2 0.080000000
## 13 CO new_cases 1470642 1437156 33486 0.023031861
## 14 KY new_cases 1366549 1356440 10109 0.007424929
## 15 SC new_cases 1507135 1504967 2168 0.001439526
##
##
##
## Raw file for cdcDaily:
## Rows: 53,520
## Columns: 15
## $ date <date> 2021-12-22, 2021-03-18, 2021-09-01, 2022-03-28, 2021-0~
## $ state <chr> "DE", "NE", "ND", "VT", "MD", "ID", "IL", "MD", "WI", "~
## $ tot_cases <dbl> 165076, 206980, 118491, 107785, 390490, 445350, 1130917~
## $ conf_cases <dbl> 151750, NA, 107475, NA, NA, 348949, 1130917, NA, 22932,~
## $ prob_cases <dbl> 13326, NA, 11016, NA, NA, 96401, 0, NA, 2548, 0, 225645~
## $ new_cases <dbl> 662, 298, 536, 467, 924, 0, 2304, 2638, 185, 24, 0, 180~
## $ pnew_case <dbl> 38, 0, 66, 35, 0, 0, 0, 0, 11, 0, 0, 0, 22, 0, NA, NA, ~
## $ tot_deaths <dbl> 2345, 2130, 1562, 585, 8549, 4918, 21336, 5410, 700, 2,~
## $ conf_death <dbl> 2133, NA, NA, NA, 8345, 4007, 19306, 5228, 694, 2, 6958~
## $ prob_death <dbl> 212, NA, NA, NA, 204, 911, 2030, 182, 6, 0, 3435, NA, 4~
## $ new_deaths <dbl> 2, 1, 1, 0, 19, 0, 63, 50, 2, 1, 0, 0, 0, 0, 0, 0, 0, 8~
## $ pnew_death <dbl> 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0~
## $ created_at <chr> "12/24/2021 12:00:00 AM", "03/20/2021 12:00:00 AM", "09~
## $ consent_cases <chr> "Agree", "Not agree", "Agree", "Not agree", "N/A", "Agr~
## $ consent_deaths <chr> "Agree", "Not agree", "Not agree", "Not agree", "Agree"~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-06-01 inp 29661 27614 2047 0.07147970
## 2 2022-06-01 hosp_ped 1493 1275 218 0.15751445
## 3 2020-08-02 hosp_ped 4087 4737 650 0.14732548
## 4 2020-07-25 hosp_ped 3940 4159 219 0.05408075
## 5 2022-06-01 hosp_adult 28203 26339 1864 0.06835100
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 GA inp 1971264 1993400 22136 0.011166646
## 2 WA inp 659079 657786 1293 0.001963755
## 3 NH hosp_ped 1015 970 45 0.045340050
## 4 ME hosp_ped 2154 2068 86 0.040738986
## 5 WV hosp_ped 5026 5143 117 0.023011112
## 6 KS hosp_ped 4460 4519 59 0.013141775
## 7 GA hosp_ped 47594 48199 605 0.012631403
## 8 AL hosp_ped 19364 19177 187 0.009703952
## 9 NM hosp_ped 7212 7279 67 0.009247119
## 10 NV hosp_ped 4879 4923 44 0.008977760
## 11 SC hosp_ped 8399 8473 74 0.008771930
## 12 UT hosp_ped 9231 9172 59 0.006411998
## 13 MO hosp_ped 36512 36705 193 0.005272000
## 14 AZ hosp_ped 25682 25815 133 0.005165349
## 15 VA hosp_ped 16626 16542 84 0.005065123
## 16 WA hosp_ped 13265 13202 63 0.004760645
## 17 HI hosp_ped 2720 2710 10 0.003683241
## 18 SD hosp_ped 4125 4111 14 0.003399709
## 19 IL hosp_ped 40566 40695 129 0.003174955
## 20 NJ hosp_ped 17971 18027 56 0.003111284
## 21 PR hosp_ped 20069 20125 56 0.002786486
## 22 NE hosp_ped 7013 7032 19 0.002705589
## 23 PA hosp_ped 50969 51103 134 0.002625598
## 24 AR hosp_ped 11525 11555 30 0.002599653
## 25 AK hosp_ped 2321 2327 6 0.002581756
## 26 WI hosp_ped 10429 10451 22 0.002107280
## 27 VT hosp_ped 497 498 1 0.002010050
## 28 MA hosp_ped 11531 11511 20 0.001735960
## 29 MD hosp_ped 15246 15272 26 0.001703912
## 30 FL hosp_ped 87309 87176 133 0.001524486
## 31 OH hosp_ped 83260 83385 125 0.001500195
## 32 CA hosp_ped 73628 73725 97 0.001316566
## 33 RI hosp_ped 3305 3301 4 0.001211020
## 34 GA hosp_adult 1637702 1659233 21531 0.013061222
## 35 WA hosp_adult 585072 583842 1230 0.002104518
## 36 NH hosp_adult 97563 97662 99 0.001014214
##
##
##
## Raw file for cdcHosp:
## Rows: 45,857
## Columns: 135
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> ~
## $ all_pediatric_inpatient_beds <dbl> ~
## $ all_pediatric_inpatient_beds_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Second_Booster_50Plus Second_Booster_50Plus_Vax_Pct Second_Booster_65Plus Second_Booster_65Plus_Vax_Pct Second_Booster_Janssen Second_Booster_Moderna Second_Booster_Pfizer Second_Booster_Unk_Manuf
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 17
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 35,608
## Columns: 91
## $ date <date> 2022-06-29, 2022-06-29, 2022-0~
## $ MMWR_week <dbl> 26, 26, 26, 26, 26, 26, 26, 26,~
## $ state <chr> "GU", "NM", "DE", "MI", "CA", "~
## $ Distributed <dbl> 330560, 4747945, 2521155, 22699~
## $ Distributed_Janssen <dbl> 24100, 188400, 100800, 949600, ~
## $ Distributed_Moderna <dbl> 88480, 1843600, 977600, 9043020~
## $ Distributed_Pfizer <dbl> 217980, 2715945, 1442755, 12706~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 196191, 226435, 258908, 227291,~
## $ Distributed_Per_100k_5Plus <dbl> 216390, 240300, 274323, 240958,~
## $ Distributed_Per_100k_12Plus <dbl> 251729, 265630, 300206, 264098,~
## $ Distributed_Per_100k_18Plus <dbl> 288588, 292904, 327341, 289423,~
## $ Distributed_Per_100k_65Plus <dbl> 2058150, 1257380, 1334610, 1285~
## $ vxa <dbl> 365588, 4085449, 1881405, 16368~
## $ Administered_5Plus <dbl> 365549, 4084293, 1880661, 16366~
## $ Administered_12Plus <dbl> 348267, 3938942, 1824929, 15886~
## $ Administered_18Plus <dbl> 313559, 3667648, 1712418, 15013~
## $ Administered_65Plus <dbl> 51989, 1113425, 584568, 4842177~
## $ Administered_Janssen <dbl> 13414, 119827, 62486, 468873, 2~
## $ Administered_Moderna <dbl> 115378, 1658104, 727328, 646501~
## $ Administered_Pfizer <dbl> 236410, 2297012, 1089219, 94318~
## $ Administered_Unk_Manuf <dbl> 386, 10506, 2372, 2318, 15598, ~
## $ Admin_Per_100k <dbl> 216980, 194839, 193210, 163896,~
## $ Admin_Per_100k_5Plus <dbl> 239295, 206711, 204632, 173730,~
## $ Admin_Per_100k_12Plus <dbl> 265213, 220369, 217303, 184833,~
## $ Admin_Per_100k_18Plus <dbl> 273745, 226260, 222337, 191432,~
## $ Admin_Per_100k_65Plus <dbl> 323697, 294864, 309449, 274282,~
## $ Recip_Administered <dbl> 365895, 4255160, 1858479, 16698~
## $ Administered_Dose1_Recip <dbl> 154870, 1867238, 820047, 673991~
## $ Administered_Dose1_Pop_Pct <dbl> 91.9, 89.1, 84.2, 67.5, 83.0, 4~
## $ Administered_Dose1_Recip_5Plus <dbl> 154834, 1866115, 819427, 673793~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 94.4, 89.2, 71.5, 88.1, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 145443, 1781472, 789949, 649181~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 95.0, 94.1, 75.5, 92.7, 5~
## $ Administered_Dose1_Recip_18Plus <dbl> 129366, 1642637, 739120, 609558~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 77.7, 93.7, 5~
## $ Administered_Dose1_Recip_65Plus <dbl> 17745, 437406, 215028, 1716056,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 4~
## $ vxc <dbl> 140333, 1509192, 682756, 605968~
## $ vxcpoppct <dbl> 83.3, 72.0, 70.1, 60.7, 72.8, 3~
## $ Series_Complete_5Plus <dbl> 140329, 1509107, 682724, 605960~
## $ Series_Complete_5PlusPop_Pct <dbl> 91.9, 76.4, 74.3, 64.3, 77.4, 4~
## $ Series_Complete_12Plus <dbl> 133100, 1444954, 659010, 584134~
## $ Series_Complete_12PlusPop_Pct <dbl> 95.0, 80.8, 78.5, 68.0, 81.6, 4~
## $ vxcgte18 <dbl> 118785, 1330134, 615581, 548244~
## $ vxcgte18pct <dbl> 95.0, 82.1, 79.9, 69.9, 82.4, 5~
## $ vxcgte65 <dbl> 16715, 363032, 184932, 1572725,~
## $ vxcgte65pct <dbl> 95.0, 95.0, 95.0, 89.1, 91.6, 3~
## $ Series_Complete_Janssen <dbl> 11228, 110899, 57466, 423472, 2~
## $ Series_Complete_Moderna <dbl> 40195, 569094, 239174, 2197962,~
## $ Series_Complete_Pfizer <dbl> 88745, 826711, 385312, 3437011,~
## $ Series_Complete_Unk_Manuf <dbl> 165, 2488, 804, 1236, 5063, 3, ~
## $ Series_Complete_Janssen_5Plus <dbl> 11227, 110889, 57462, 423458, 2~
## $ Series_Complete_Moderna_5Plus <dbl> 40193, 569057, 239163, 2197936,~
## $ Series_Complete_Pfizer_5Plus <dbl> 88744, 826674, 385295, 3436979,~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 165, 2487, 804, 1235, 5061, 3, ~
## $ Series_Complete_Janssen_12Plus <dbl> 11226, 110880, 57455, 423439, 2~
## $ Series_Complete_Moderna_12Plus <dbl> 40193, 568999, 239160, 2197875,~
## $ Series_Complete_Pfizer_12Plus <dbl> 81517, 762602, 361596, 3218815,~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 164, 2473, 799, 1215, 4980, 3, ~
## $ Series_Complete_Janssen_18Plus <dbl> 11217, 110737, 57405, 423120, 2~
## $ Series_Complete_Moderna_18Plus <dbl> 40173, 568568, 239017, 2197100,~
## $ Series_Complete_Pfizer_18Plus <dbl> 67231, 648399, 318385, 2861129,~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 164, 2430, 774, 1099, 4662, 3, ~
## $ Series_Complete_Janssen_65Plus <dbl> 642, 21439, 10024, 71373, 20281~
## $ Series_Complete_Moderna_65Plus <dbl> 6825, 170464, 76713, 788262, 27~
## $ Series_Complete_Pfizer_65Plus <dbl> 9205, 169969, 97812, 712464, 24~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 43, 1160, 383, 626, 1490, 0, 36~
## $ Additional_Doses <dbl> 69361, 776169, 323480, 3353818,~
## $ Additional_Doses_Vax_Pct <dbl> 49.4, 51.4, 47.4, 55.3, 54.5, 3~
## $ Additional_Doses_12Plus <dbl> 69087, 770329, 322088, 3341400,~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 51.9, 53.3, 48.9, 57.2, 56.6, 3~
## $ Additional_Doses_18Plus <dbl> 64829, 734378, 309800, 3231323,~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 54.6, 55.2, 50.3, 58.9, 58.7, 4~
## $ Additional_Doses_50Plus <dbl> 33243, 459183, 219856, 2151063,~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 71.4, 64.7, 61.8, 69.2, 69.8, 5~
## $ Additional_Doses_65Plus <dbl> 13181, 254948, 130474, 1217144,~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 78.9, 70.2, 70.6, 77.4, 77.3, 5~
## $ Additional_Doses_Moderna <dbl> 25618, 323485, 138013, 1482974,~
## $ Additional_Doses_Pfizer <dbl> 42023, 441374, 180140, 1822184,~
## $ Additional_Doses_Janssen <dbl> 1714, 10969, 5252, 48489, 23425~
## $ Additional_Doses_Unk_Manuf <dbl> 6, 341, 75, 171, 703, 0, 175, 1~
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,~
## $ Second_Booster_50Plus <dbl> 8765, 156782, 64930, 599983, 26~
## $ Second_Booster_50Plus_Vax_Pct <dbl> 26.4, 34.1, 29.5, 27.9, 31.9, 1~
## $ Second_Booster_65Plus <dbl> 4701, 107117, 47944, 426384, 16~
## $ Second_Booster_65Plus_Vax_Pct <dbl> 35.7, 42.0, 36.7, 35.0, 39.6, 1~
## $ Second_Booster_Janssen <dbl> 5, 235, 67, 404, 2029, 0, 195, ~
## $ Second_Booster_Moderna <dbl> 3885, 76133, 31765, 296665, 141~
## $ Second_Booster_Pfizer <dbl> 5297, 88094, 35113, 325197, 136~
## $ Second_Booster_Unk_Manuf <dbl> 0, 136, 11, 27, 72, 0, 32, 36, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.93e+10 4.48e+8 87042181 996716 52628
## 2 after 2.91e+10 4.46e+8 86169240 991589 45492
## 3 pctchg 6.01e- 3 4.47e-3 0.0100 0.00514 0.136
##
##
## Processed for cdcDaily:
## Rows: 45,492
## Columns: 6
## $ date <date> 2021-12-22, 2021-03-18, 2021-09-01, 2022-03-28, 2021-03-11~
## $ state <chr> "DE", "NE", "ND", "VT", "MD", "ID", "IL", "MD", "WI", "CA",~
## $ tot_cases <dbl> 165076, 206980, 118491, 107785, 390490, 445350, 1130917, 23~
## $ tot_deaths <dbl> 2345, 2130, 1562, 585, 8549, 4918, 21336, 5410, 700, 2, 103~
## $ new_cases <dbl> 662, 298, 536, 467, 924, 0, 2304, 2638, 185, 24, 0, 180, 55~
## $ new_deaths <dbl> 2, 1, 1, 0, 19, 0, 63, 50, 2, 1, 0, 0, 0, 0, 0, 0, 8, 53, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.85e+7 4.20e+7 1112611 45857
## 2 after 4.83e+7 4.18e+7 1090781 43866
## 3 pctchg 5.16e-3 4.93e-3 0.0196 0.0434
##
##
## Processed for cdcHosp:
## Rows: 43,866
## Columns: 5
## $ date <date> 2020-12-19, 2020-10-04, 2020-10-02, 2020-09-25, 2020-09-23~
## $ state <chr> "SD", "WY", "VT", "AK", "AK", "VT", "VT", "VT", "VT", "SD",~
## $ inp <dbl> 335, 58, 0, 52, 51, 1, 4, 5, 3, 109, 2, 37, 9, 81, 80, 42, ~
## $ hosp_adult <dbl> 332, 57, 0, 51, 48, 1, 4, 5, 3, 74, 2, 36, 6, 76, 79, 39, 5~
## $ hosp_ped <dbl> 3, 1, 0, 1, 2, 0, 0, 0, 0, 29, 0, 1, 1, 0, 1, 2, 0, 2, 0, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.03e+11 1.66e+11 1475157. 4.22e+10 2189089. 1.54e+11 1739833
## 2 after 1.94e+11 8.02e+10 1235107. 2.04e+10 1940160. 7.43e+10 1472279.
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 0.114 5.16e- 1 0.154
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 28,152
## Columns: 9
## $ date <date> 2022-06-29, 2022-06-29, 2022-06-29, 2022-06-29, 2022-06-2~
## $ state <chr> "NM", "DE", "MI", "CA", "WI", "DC", "NY", "TN", "AK", "AL"~
## $ vxa <dbl> 4085449, 1881405, 16368051, 77764209, 10648880, 1594848, 4~
## $ vxc <dbl> 1509192, 682756, 6059681, 28773716, 3849301, 537548, 15135~
## $ vxcpoppct <dbl> 72.0, 70.1, 60.7, 72.8, 66.1, 76.2, 77.8, 54.9, 62.7, 51.6~
## $ vxcgte65 <dbl> 363032, 184932, 1572725, 5348260, 983408, 86544, 3082249, ~
## $ vxcgte65pct <dbl> 95.0, 95.0, 89.1, 91.6, 95.0, 95.0, 93.5, 85.2, 86.8, 82.9~
## $ vxcgte18 <dbl> 1330134, 615581, 5482448, 25229699, 3453855, 490080, 13543~
## $ vxcgte18pct <dbl> 82.1, 79.9, 69.9, 82.4, 75.8, 84.9, 87.8, 64.9, 73.5, 61.6~
##
## Integrated per capita data file:
## Rows: 45,756
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220704, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220704 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220704.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 494,918
## Columns: 128
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 9372
## 2 Critical Access Hospitals 132455
## 3 Long Term 33970
## 4 Short Term 319121
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 44
## 2 GU 198
## 3 MP 95
## 4 PR 5407
## 5 VI 198
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 50874 443132 912 494918
## 2 all_adult_hospital_inpatient_bed_occupi~ 3319 450806 40793 494918
## 3 icu_beds_used_7_day_avg 1653 433066 60199 494918
## 4 inpatient_beds_7_day_avg 1733 491216 1969 494918
## 5 staffed_icu_adult_patients_confirmed_an~ 4245 342951 147722 494918
## 6 total_adult_patients_hospitalized_confi~ 2363 338877 153678 494918
## 7 total_beds_7_day_avg 44507 449926 485 494918
## 8 total_icu_beds_7_day_avg 2067 467915 24936 494918## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220704, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220704 <- postProcessCDCDaily(cdc_daily_220704,
dataThruLabel="Jun 2022",
keyDatesBurden=c("2022-06-30", "2021-12-31",
"2021-06-30", "2020-12-31"
),
keyDatesVaccine=c("2022-06-29", "2022-02-28",
"2021-10-31", "2021-06-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220704 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220704,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220704$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 x 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con~ 43797 45857
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con~ 263705 45857
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con~ 384671 45857
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con~ 470926 45857
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con~ 751006 45857
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con~ 970735 45857
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con~ 958201 45857
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con~ 838101 45857
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus~ 36051 45857
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus~ 241076 45857
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus~ 315976 45857
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus~ 321335 45857
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus~ 507664 45857
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus~ 695927 45857
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus~ 675237 45857
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus~ 614420 45857
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid~ 145408 45857
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid~ 344599 45857Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220704)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,960 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 6,950 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity plots are also updated:
modStateHosp_20220704 <- hospitalCapacityCDCDaily(indivHosp_20220704, plotSub="August 2020 to June 2022")
The latest data are downloaded and processed:
# Update to helperSummaryMap for updated usmap::usmap_transform
helperSummaryMap <- function(df,
mapLevel="states",
keyCol="state",
values="cluster",
discreteValues=NULL,
legend.position="right",
labelScale=TRUE,
extraArgs=list(),
countOnly=FALSE,
textLabel=c(),
...
) {
# FUNCTION ARGUMENTS:
# df: a data frame containing a level of geography and an associated cluster
# mapLevel: a parameter for whether the map is "states" or "counties"
# keyCol: the key column for plotting (usmap::plot_usmap is particular, and this must be 'state' or 'fips')
# values: the character name of the field containing the data to be plotted
# discreteValues: boolean for whether the values are discrete (if not, use continuous)
# NULL means infer from data
# legend.position: character for the location of the legend in the plot
# labelScale: boolean, should an scale_fill_ be created? Use FALSE if contained in extraArgs
# extraArgs: list of other arguments that will be appended as '+' to the end of the usmap::plot_usmap call
# countOnly: should a bar plot of counts only be produced?
# textLabel: a list of elements that should be labelled as text on the plot (too small to see)
# ...: other parameters to be passed to usmap::plot_usmap (e.g., labels, include, exclude, etc.)
# Modify the data frame to contain only the relevant data
df <- df %>%
select(all_of(c(keyCol, values))) %>%
distinct()
# Determine the type of data being plotted
if (is.null(discreteValues)) discreteValues <- !is.numeric(df[[values]])
# Convert data type if needed
if (isTRUE(discreteValues) & is.numeric(df[[values]]))
df[[values]] <- factor(df[[values]])
# If count only is needed, create a count map; otherwise create a map
if (isTRUE(countOnly)) {
gg <- df %>%
ggplot(aes(x=fct_rev(get(values)))) +
geom_bar(aes_string(fill=values)) +
stat_count(aes(label=..count.., y=..count../2),
geom="text",
position="identity",
fontface="bold"
) +
coord_flip() +
labs(y="Number of members", x="")
} else {
gg <- usmap::plot_usmap(regions=mapLevel, data=df, values=values, ...)
if (length(textLabel) > 0) {
labDF <- df %>%
filter(get(keyCol) %in% textLabel) %>%
mutate(rk=match(get(keyCol), textLabel)) %>%
arrange(rk) %>%
mutate(lon=-70.1-seq(0, 0.8*length(textLabel)-0.8, by=0.8),
lat=40.1-seq(0, 1.5*length(textLabel)-1.5, by=1.5)
) %>%
select(lon, lat, everything()) %>%
usmap::usmap_transform(output_names=c("lon.1", "lat.1"))
gg <- gg + geom_text(data=labDF,
aes(x=lon.1, y=lat.1, label=paste(get(keyCol), get(values))),
size=3.25
)
}
}
# Position the legend as requested
gg <- gg + theme(legend.position=legend.position)
# Create the scale if appropriate
if (isTRUE(labelScale)) gg <- gg +
if(isTRUE(discreteValues)) scale_fill_discrete(values) else scale_fill_continuous(values)
# Apply extra arguments
for (ctr in seq_along(extraArgs)) gg <- gg + extraArgs[[ctr]]
# Return the map object
gg
}
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220805.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220805.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220805.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220704")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220704")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220704")$dfRaw$vax
)
cdc_daily_220805 <- readRunCDCDaily(thruLabel="Aug 3, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_220805.csv## Rows: 55500 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-06-26 new_deaths 72 29 43 0.85148515
## 2 2022-06-25 new_deaths 109 62 47 0.54970760
## 3 2022-06-19 new_deaths 114 89 25 0.24630542
## 4 2022-06-18 new_deaths 97 82 15 0.16759777
## 5 2022-06-27 new_deaths 291 247 44 0.16356877
## 6 2022-06-30 new_deaths 486 415 71 0.15760266
## 7 2022-06-20 new_deaths 145 127 18 0.13235294
## 8 2022-06-28 new_deaths 608 537 71 0.12401747
## 9 2022-05-29 new_deaths 58 52 6 0.10909091
## 10 2022-06-29 new_deaths 583 524 59 0.10659440
## 11 2022-06-24 new_deaths 454 410 44 0.10185185
## 12 2022-07-01 new_deaths 538 487 51 0.09951220
## 13 2022-06-12 new_deaths 95 86 9 0.09944751
## 14 2022-05-30 new_deaths 83 76 7 0.08805031
## 15 2022-06-23 new_deaths 499 459 40 0.08350731
## 16 2022-05-14 new_deaths 82 76 6 0.07594937
## 17 2022-06-16 new_deaths 304 286 18 0.06101695
## 18 2022-06-05 new_deaths 124 117 7 0.05809129
## 19 2022-06-13 new_deaths 296 280 16 0.05555556
## 20 2022-06-22 new_deaths 626 593 33 0.05414274
## 21 2022-07-01 new_cases 172854 158017 14837 0.08968450
## 22 2022-06-30 new_cases 118066 110590 7476 0.06539081
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 6398010 6382749 15261 0.002388121
## 2 NC tot_deaths 10480735 10467772 12963 0.001237606
## 3 CO tot_cases 493149559 491619869 1529690 0.003106697
## 4 KY new_deaths 16365 16182 183 0.011245276
## 5 NC new_deaths 25435 25211 224 0.008845713
## 6 FL new_deaths 76444 75891 553 0.007260314
## 7 AL new_deaths 19823 19776 47 0.002373797
## 8 KY new_cases 1416476 1406705 9771 0.006921979
## 9 CO new_cases 1547593 1537672 9921 0.006431214
## 10 SC new_cases 1555208 1546406 8802 0.005675755
## 11 NC new_cases 2883706 2869560 14146 0.004917555
## 12 CT new_cases 817234 819923 2689 0.003284963
## 13 FL new_cases 6504443 6493975 10468 0.001610658
##
##
##
## Raw file for cdcDaily:
## Rows: 55,500
## Columns: 15
## $ date <date> 2022-01-14, 2020-07-11, 2022-01-02, 2020-02-04, 2022-0…
## $ state <chr> "KS", "TN", "AS", "AR", "AK", "PA", "TX", "PW", "AS", "…
## $ tot_cases <dbl> 621273, 59582, 11, 0, 251425, 86552, 361125, 0, 0, 1226…
## $ conf_cases <dbl> 470516, 59137, NA, NA, NA, 84260, NA, NA, NA, NA, NA, N…
## $ prob_cases <dbl> 150757, 445, NA, NA, NA, 2292, NA, NA, NA, NA, NA, NA, …
## $ new_cases <dbl> 19414, 1964, 0, 0, 0, 459, 9507, 0, 0, 28, 2, 8, 2293, …
## $ pnew_case <dbl> 6964, 28, 0, NA, 0, 18, 0, 0, 0, 5, 0, 0, 552, 46, 70, …
## $ tot_deaths <dbl> 7162, 723, 0, 0, 1252, 6426, 7981, 0, 0, 1967, 0, 17, 1…
## $ conf_death <dbl> NA, 697, NA, NA, NA, NA, NA, NA, NA, 1601, 0, NA, 18646…
## $ prob_death <dbl> NA, 26, NA, NA, NA, NA, NA, NA, NA, 366, 0, NA, 0, 360,…
## $ new_deaths <dbl> 21, 13, 0, 0, 0, 0, 281, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, …
## $ pnew_death <dbl> 4, 0, 0, NA, 0, -264, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at <chr> "01/15/2022 02:59:30 PM", "07/10/2020 12:00:00 AM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Not agree", "N/A", "Agree", "Not…
## $ consent_deaths <chr> "N/A", "Agree", NA, "Not agree", "N/A", "Not agree", "N…
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_220805.csv## Rows: 47585 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 4594 3940 654 0.1532693
## 2 2020-08-02 hosp_ped 4712 4087 625 0.1420616
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AS inp 543 541 2 0.003690037
## 2 NH hosp_ped 1122 1073 49 0.044646925
## 3 WV hosp_ped 5454 5257 197 0.036784614
## 4 KS hosp_ped 4498 4589 91 0.020028612
## 5 ID hosp_ped 3835 3765 70 0.018421053
## 6 NV hosp_ped 5124 5190 66 0.012798138
## 7 VA hosp_ped 17305 17454 149 0.008573319
## 8 AR hosp_ped 11989 11892 97 0.008123613
## 9 KY hosp_ped 19577 19435 142 0.007279811
## 10 NM hosp_ped 7703 7648 55 0.007165657
## 11 TN hosp_ped 21368 21220 148 0.006950315
## 12 MO hosp_ped 37949 37698 251 0.006636086
## 13 MA hosp_ped 12147 12225 78 0.006400788
## 14 NJ hosp_ped 18742 18632 110 0.005886445
## 15 UT hosp_ped 9659 9715 56 0.005780944
## 16 IN hosp_ped 17135 17042 93 0.005442256
## 17 ME hosp_ped 2259 2249 10 0.004436557
## 18 PR hosp_ped 21623 21710 87 0.004015416
## 19 MS hosp_ped 11012 10974 38 0.003456745
## 20 SC hosp_ped 8605 8634 29 0.003364464
## 21 IL hosp_ped 42493 42377 116 0.002733593
## 22 CO hosp_ped 21168 21111 57 0.002696374
## 23 PA hosp_ped 53172 53065 107 0.002014364
## 24 NC hosp_ped 29449 29505 56 0.001899786
## 25 FL hosp_ped 89852 90020 168 0.001867995
## 26 AZ hosp_ped 26450 26403 47 0.001778518
## 27 RI hosp_ped 3473 3479 6 0.001726122
## 28 OR hosp_ped 10977 10993 16 0.001456532
## 29 MD hosp_ped 16245 16224 21 0.001293542
## 30 HI hosp_ped 3171 3167 4 0.001262228
## 31 WY hosp_ped 823 822 1 0.001215805
## 32 SD hosp_ped 4194 4199 5 0.001191469
## 33 DE hosp_ped 5065 5059 6 0.001185302
## 34 NY hosp_ped 71294 71213 81 0.001136786
## 35 TX hosp_ped 114450 114322 128 0.001119018
## 36 CA hosp_ped 78112 78026 86 0.001101590
## 37 ND hosp_ped 3651 3647 4 0.001096191
## 38 AS hosp_adult 529 527 2 0.003787879
##
##
##
## Raw file for cdcHosp:
## Rows: 47,585
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_220805.csv## Rows: 35928 Columns: 93
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (91): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Additional_Doses_5Plus Additional_Doses_5Plus_Vax_Pct
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 35,928
## Columns: 93
## $ date <date> 2022-08-03, 2022-08-03, 2022-0…
## $ MMWR_week <dbl> 31, 31, 31, 31, 31, 31, 31, 31,…
## $ state <chr> "AK", "LA", "PW", "FL", "GU", "…
## $ Distributed <dbl> 1721465, 8927950, 46890, 520299…
## $ Distributed_Janssen <dbl> 85800, 327400, 3800, 2425100, 2…
## $ Distributed_Moderna <dbl> 667420, 3668180, 30000, 1935532…
## $ Distributed_Pfizer <dbl> 967445, 4927870, 13090, 3023546…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 235319, 192049, 217769, 242251,…
## $ Distributed_Per_100k_5Plus <dbl> 252984, 205367, 230158, 255827,…
## $ Distributed_Per_100k_12Plus <dbl> 282728, 227561, 251488, 278294,…
## $ Distributed_Per_100k_18Plus <dbl> 312107, 250703, 282759, 301661,…
## $ Distributed_Per_100k_65Plus <dbl> 1879580, 1204820, 2353920, 1156…
## $ vxa <dbl> 1193878, 6490846, 49270, 390355…
## $ Administered_5Plus <dbl> 1190378, 6485568, 49256, 390025…
## $ Administered_12Plus <dbl> 1143663, 6351067, 46584, 381763…
## $ Administered_18Plus <dbl> 1064124, 6011787, 42928, 362036…
## $ Administered_65Plus <dbl> 244166, 1880520, 5330, 13019177…
## $ Administered_Janssen <dbl> 45975, 201008, 2356, 1491938, 1…
## $ Administered_Moderna <dbl> 460982, 2648043, 37718, 1447846…
## $ Administered_Pfizer <dbl> 685618, 3638334, 9031, 22909920…
## $ Administered_Unk_Manuf <dbl> 1301, 3353, 165, 154533, 394, 3…
## $ Admin_Per_100k <dbl> 163200, 139624, 228822, 181749,…
## $ Admin_Per_100k_5Plus <dbl> 174936, 149185, 241771, 191772,…
## $ Admin_Per_100k_12Plus <dbl> 187832, 161880, 249847, 204195,…
## $ Admin_Per_100k_18Plus <dbl> 192929, 168815, 258868, 209903,…
## $ Admin_Per_100k_65Plus <dbl> 266592, 253774, 267570, 289486,…
## $ Recip_Administered <dbl> 1208382, 6468369, 49645, 387760…
## $ Administered_Dose1_Recip <dbl> 520599, 2874375, 20539, 1731201…
## $ Administered_Dose1_Pop_Pct <dbl> 71.2, 61.8, 95.0, 80.6, 92.4, 0…
## $ Administered_Dose1_Recip_5Plus <dbl> 518121, 2870474, 20526, 1728636…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 76.1, 66.0, 95.0, 85.0, 95.0, 0…
## $ Administered_Dose1_Recip_12Plus <dbl> 494499, 2793348, 19099, 1684615…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 81.2, 71.2, 95.0, 90.1, 95.0, 0…
## $ Administered_Dose1_Recip_18Plus <dbl> 457624, 2618633, 17567, 1586212…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 83.0, 73.5, 95.0, 92.0, 95.0, 0…
## $ Administered_Dose1_Recip_65Plus <dbl> 88238, 681022, 1874, 4841075, 1…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.9, 94.1, 95.0, 95.0, 0…
## $ vxc <dbl> 461227, 2517249, 18308, 1459286…
## $ vxcpoppct <dbl> 63.0, 54.1, 85.0, 67.9, 83.6, 0…
## $ Series_Complete_5Plus <dbl> 460519, 2516780, 18307, 1458923…
## $ Series_Complete_5PlusPop_Pct <dbl> 67.7, 57.9, 89.9, 71.7, 92.2, 0…
## $ Series_Complete_12Plus <dbl> 440334, 2461210, 17225, 1424111…
## $ Series_Complete_12PlusPop_Pct <dbl> 72.3, 62.7, 92.4, 76.2, 95.0, 0…
## $ vxcgte18 <dbl> 406880, 2316905, 15778, 1342286…
## $ vxcgte18pct <dbl> 73.8, 65.1, 95.0, 77.8, 95.0, 0…
## $ vxcgte65 <dbl> 79796, 641441, 1809, 4150448, 1…
## $ vxcgte65pct <dbl> 87.1, 86.6, 90.8, 92.3, 95.0, 0…
## $ Series_Complete_Janssen <dbl> 42125, 182512, 2360, 1374210, 1…
## $ Series_Complete_Moderna <dbl> 162208, 969622, 12715, 4930391,…
## $ Series_Complete_Pfizer <dbl> 256605, 1363742, 3152, 8243949,…
## $ Series_Complete_Unk_Manuf <dbl> 282, 1295, 81, 44095, 169, 0, 2…
## $ Series_Complete_Janssen_5Plus <dbl> 42123, 182490, 2360, 1373388, 1…
## $ Series_Complete_Moderna_5Plus <dbl> 161887, 969370, 12715, 4927990,…
## $ Series_Complete_Pfizer_5Plus <dbl> 256225, 1363606, 3151, 8243747,…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 282, 1292, 81, 43923, 169, 0, 2…
## $ Series_Complete_Janssen_12Plus <dbl> 42121, 182478, 2360, 1373333, 1…
## $ Series_Complete_Moderna_12Plus <dbl> 161820, 969336, 12715, 4927746,…
## $ Series_Complete_Pfizer_12Plus <dbl> 236115, 1308104, 2069, 7896721,…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 277, 1270, 81, 43131, 168, 0, 1…
## $ Series_Complete_Janssen_18Plus <dbl> 41956, 182279, 2360, 1372347, 1…
## $ Series_Complete_Moderna_18Plus <dbl> 161382, 968751, 12715, 4926033,…
## $ Series_Complete_Pfizer_18Plus <dbl> 203276, 1164614, 622, 7082202, …
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 265, 1244, 81, 42108, 168, 0, 1…
## $ Series_Complete_Janssen_65Plus <dbl> 3720, 22498, 227, 215678, 645, …
## $ Series_Complete_Moderna_65Plus <dbl> 43746, 305326, 1541, 1989561, 6…
## $ Series_Complete_Pfizer_65Plus <dbl> 32272, 313396, 39, 1922722, 921…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 58, 219, 2, 22443, 45, 0, 325, …
## $ Additional_Doses <dbl> 215249, 1042687, 12000, 6159483…
## $ Additional_Doses_Vax_Pct <dbl> 46.7, 41.4, 65.5, 42.2, 50.5, 3…
## $ Additional_Doses_5Plus <dbl> 215238, 1042609, 12000, 6159391…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 46.7, 41.4, 65.5, 42.2, 50.5, 3…
## $ Additional_Doses_12Plus <dbl> 212925, 1039965, 11834, 6134941…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 48.4, 42.3, 68.7, 43.1, 53.0, 3…
## $ Additional_Doses_18Plus <dbl> 202938, 1017274, 11146, 5979633…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 49.9, 43.9, 70.6, 44.5, 55.6, 3…
## $ Additional_Doses_50Plus <dbl> 119524, 731352, 4804, 4352080, …
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 65.6, 57.2, 80.1, 56.3, 72.4, 5…
## $ Additional_Doses_65Plus <dbl> 60758, 431421, 1572, 2663002, 1…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 76.1, 67.3, 86.9, 64.2, 79.8, 7…
## $ Additional_Doses_Moderna <dbl> 93205, 462199, 10843, 2599915, …
## $ Additional_Doses_Pfizer <dbl> 118869, 566485, 1155, 3440935, …
## $ Additional_Doses_Janssen <dbl> 3109, 13884, 2, 108984, 1777, 6…
## $ Additional_Doses_Unk_Manuf <dbl> 66, 110, 0, 9607, 6, 22, 148, 4…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, 217…
## $ Second_Booster_50Plus <dbl> 38932, 172409, 1099, 1260797, 1…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 32.6, 23.6, 22.9, 29.0, 31.0, 1…
## $ Second_Booster_65Plus <dbl> 23911, 122667, 374, 920552, 546…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 39.4, 28.4, 23.8, 34.6, 40.9, 2…
## $ Second_Booster_Janssen <dbl> 54, 154, 0, 1932, 5, 3, 778, 19…
## $ Second_Booster_Moderna <dbl> 22049, 82271, 1121, 661693, 471…
## $ Second_Booster_Pfizer <dbl> 19472, 99685, 20, 657146, 6392,…
## $ Second_Booster_Unk_Manuf <dbl> 14, 9, 0, 3661, 0, 2, 36, 14924…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.23e+10 4.82e+8 91032457 1010326 54575
## 2 after 3.20e+10 4.80e+8 90063255 1004965 47175
## 3 pctchg 6.40e- 3 4.51e-3 0.0106 0.00531 0.136
##
##
## Processed for cdcDaily:
## Rows: 47,175
## Columns: 6
## $ date <date> 2022-01-14, 2020-07-11, 2020-02-04, 2022-05-30, 2020-06-22…
## $ state <chr> "KS", "TN", "AR", "AK", "PA", "TX", "SD", "IN", "HI", "OH",…
## $ tot_cases <dbl> 621273, 59582, 0, 251425, 86552, 361125, 122688, 5, 661, 10…
## $ tot_deaths <dbl> 7162, 723, 0, 1252, 6426, 7981, 1967, 0, 17, 18646, 17818, …
## $ new_cases <dbl> 19414, 1964, 0, 0, 459, 9507, 28, 2, 8, 2293, 451, 69, 1, 0…
## $ new_deaths <dbl> 21, 13, 0, 0, 0, 281, 0, 0, 0, 0, 5, 0, 1, 0, 0, 121, 30, 3…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.98e+7 4.32e+7 1171904 47585
## 2 after 4.96e+7 4.30e+7 1148807 45498
## 3 pctchg 5.27e-3 5.04e-3 0.0197 0.0439
##
##
## Processed for cdcHosp:
## Rows: 45,498
## Columns: 5
## $ date <date> 2020-10-16, 2020-10-10, 2020-10-09, 2020-10-05, 2020-10-01…
## $ state <chr> "VT", "AL", "VT", "AK", "VT", "RI", "VT", "VT", "RI", "RI",…
## $ inp <dbl> 2, 983, 0, 44, 1, 91, 3, 1, 85, 78, 50, 3, 36, 22, 191, 69,…
## $ hosp_adult <dbl> 1, 961, 0, 43, 1, 90, 2, 1, 84, 78, 49, 3, 35, 22, 189, 66,…
## $ hosp_ped <dbl> 1, 22, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 185, 0,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.09e+11 1.68e+11 1495372. 4.27e+10 2.22e+6 1.56e+11 1.76e+6 3.59e+4
## 2 after 1.97e+11 8.13e+10 1251882. 2.07e+10 1.96e+6 7.53e+10 1.49e+6 2.84e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,407
## Columns: 9
## $ date <date> 2022-08-03, 2022-08-03, 2022-08-03, 2022-08-03, 2022-08-0…
## $ state <chr> "AK", "LA", "FL", "NJ", "SC", "MT", "OH", "GA", "WY", "MI"…
## $ vxa <dbl> 1193878, 6490846, 39035508, 17969587, 7864789, 1658980, 18…
## $ vxc <dbl> 461227, 2517249, 14592869, 6823997, 2987802, 615118, 69054…
## $ vxcpoppct <dbl> 63.0, 54.1, 67.9, 76.8, 58.0, 57.6, 59.1, 55.7, 51.7, 60.8…
## $ vxcgte65 <dbl> 79796, 641441, 4150448, 1395646, 834250, 179844, 1805787, …
## $ vxcgte65pct <dbl> 87.1, 86.6, 92.3, 94.6, 89.0, 87.1, 88.2, 84.6, 85.2, 89.2…
## $ vxcgte18 <dbl> 406880, 2316905, 13422863, 6036240, 2728794, 560758, 62552…
## $ vxcgte18pct <dbl> 73.8, 65.1, 77.8, 86.9, 67.6, 66.7, 68.7, 66.0, 61.7, 70.0…
##
## Integrated per capita data file:
## Rows: 47,388
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_220805, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/cdc_daily_220805.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULL# Run for latest data, save as RDS
indivHosp_20220805 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220805.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220805.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 235862 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 235,862
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4440
## 2 Critical Access Hospitals 63222
## 3 Long Term 16209
## 4 Short Term 151991
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 48
## 2 GU 94
## 3 MP 42
## 4 PR 2539
## 5 VI 94
##
## Record types for key metrics
## # A tibble: 8 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 54898 180522 442 235862
## 2 all_adult_hospital_inpatient_bed_occupied_7_day_… 119 216617 19126 235862
## 3 icu_beds_used_7_day_avg 54 207812 27996 235862
## 4 inpatient_beds_7_day_avg 65 234891 906 235862
## 5 staffed_icu_adult_patients_confirmed_and_suspect… 150 162689 73023 235862
## 6 total_adult_patients_hospitalized_confirmed_and_… 112 159480 76270 235862
## 7 total_beds_7_day_avg 53263 182340 259 235862
## 8 total_icu_beds_7_day_avg 61 223623 12178 235862
## # … with abbreviated variable names ¹Positive, ²`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220805, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220805.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220805 <- postProcessCDCDaily(cdc_daily_220805,
dataThruLabel="Jul 2022",
keyDatesBurden=c("2022-07-31", "2022-01-31",
"2021-07-31", "2021-01-31"
),
keyDatesVaccine=c("2022-07-27", "2022-03-31",
"2021-11-30", "2021-07-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220805 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220805,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220805$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.54e4 47585
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.75e5 47585
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 3.99e5 47585
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.84e5 47585
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.73e5 47585
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.00e6 47585
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.00e6 47585
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 8.87e5 47585
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.72e4 47585
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.48e5 47585
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.26e5 47585
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.30e5 47585
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.22e5 47585
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.17e5 47585
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 6.97e5 47585
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.35e5 47585
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.56e5 47585
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.58e5 47585Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220805)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 7,344 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 7,334 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity plots are also updated:
modStateHosp_20220805 <- hospitalCapacityCDCDaily(indivHosp_20220805, plotSub="August 2020 to July 2022")## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 4 row(s) containing missing values (geom_path).
Hospital data appear to be anomalous prior to mid-2021. Record counts are compared:
dfTemp <- indivHosp_20220805 %>%
count(state, collection_week) %>%
bind_rows(count(readFromRDS("indivHosp_20220704"), state, collection_week), .id="source") %>%
mutate(source=c("1"="Aug 2022", "2"="Jul 2022")[source]) %>%
pivot_wider(c(state, collection_week), names_from="source", values_from="n") %>%
mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), 0, x))) %>%
pivot_longer(-c(state, collection_week), names_to="source", values_to="n")
dfTemp## # A tibble: 11,528 × 4
## state collection_week source n
## <chr> <date> <chr> <dbl>
## 1 AK 2020-08-07 Aug 2022 1
## 2 AK 2020-08-07 Jul 2022 16
## 3 AK 2020-09-11 Aug 2022 1
## 4 AK 2020-09-11 Jul 2022 16
## 5 AK 2021-08-27 Aug 2022 14
## 6 AK 2021-08-27 Jul 2022 14
## 7 AK 2021-09-03 Aug 2022 14
## 8 AK 2021-09-03 Jul 2022 14
## 9 AK 2021-09-10 Aug 2022 13
## 10 AK 2021-09-10 Jul 2022 14
## # … with 11,518 more rows
## # ℹ Use `print(n = ...)` to see more rowsdfTemp %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=source, color=source)) +
facet_wrap(~state, scales="free_y") +
labs(title="Number of hospitals with CDC-reported hospital records",
subtitle="August 2020 to July 2022",
x=NULL,
y="Hospitals reporting in CDC data"
) +
scale_color_discrete("Data pulled in:")
indivHosp_20220805 %>%
skinnyHHS() %>%
group_by(state, collection_week) %>%
summarize(across(where(is.numeric),
.fns=function(x) sum(ifelse(is.na(x), 0, ifelse(x==-999999, 0, x)))
),
.groups="drop"
) %>%
full_join(tibble::tibble(collection_week=rep(seq.Date(as.Date("2020-07-31"), as.Date("2022-07-24"), by=7), 51),
state=rep(c(state.abb, "DC"), each=104)
),
by=c("collection_week", "state")
) %>%
pivot_longer(-c(state, collection_week)) %>%
filter(str_detect(name, "icu")) %>%
ggplot(aes(x=collection_week, y=ifelse(is.na(value), 0, value))) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~state, scales="free_y") +
labs(title="Aug 2022 ICU data", x=NULL, y="Sum of ICU Beds") +
scale_color_discrete("Metric")
The metrics process is converted to functional form:
checkHospitalMetrics <- function(df,
startDate,
endDate,
byDays=7,
keyVars=NULL,
createPlot=TRUE,
returnData=!isTRUE(createPlot),
plotTitle=NULL
) {
# FUNCTION ARGUMENTS:
# df: processed hospital-level data frame
# startDate: starting date for adding 0 if record does not exist
# endDate: ending date for adding 0 if record does not exist
# byDays: peiodicity for the date data (e.g., 7 is weekly)
# keyVars: variables to include in data frame and plot (NULL means all)
# createPlot: boolean, should the plot be created?
# returnData: boolean, should the data be returned?
# plotTitle title for the plot (NULL means no title)
keyDays <- seq.Date(as.Date(startDate), as.Date(endDate), by=byDays)
dfPlot <- df %>%
skinnyHHS() %>%
group_by(state, collection_week) %>%
summarize(across(where(is.numeric),
.fns=function(x) sum(ifelse(is.na(x), 0, ifelse(x==-999999, 0, x)))
),
.groups="drop"
) %>%
full_join(tibble::tibble(collection_week=rep(keyDays, 51),
state=rep(c(state.abb, "DC"), each=length(keyDays))
),
by=c("collection_week", "state")
) %>%
pivot_longer(-c(state, collection_week))
if(!is.null(keyVars)) dfPlot <- dfPlot %>% filter(name %in% all_of(keyVars))
if(isTRUE(createPlot)) {
p1 <- dfPlot %>%
ggplot(aes(x=collection_week, y=ifelse(is.na(value), 0, value))) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~state, scales="free_y") +
labs(title=plotTitle, x=NULL, y="Sum of Metric") +
scale_color_discrete("Metric")
print(p1)
}
if(isTRUE(returnData)) return(dfPlot)
}
icuVars <- c("adult_icu_covid", "icu_beds", "icu_beds_occupied")
checkHospitalMetrics(indivHosp_20220805,
startDate="2020-07-31",
endDate="2022-07-24",
keyVars=icuVars,
plotTitle="Aug 2022 ICU Data",
returnData=TRUE
)
## # A tibble: 15,912 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-08-07 icu_beds 0
## 2 AK 2020-08-07 icu_beds_occupied 0
## 3 AK 2020-08-07 adult_icu_covid 0
## 4 AK 2020-09-11 icu_beds 14
## 5 AK 2020-09-11 icu_beds_occupied 7.3
## 6 AK 2020-09-11 adult_icu_covid 0
## 7 AK 2021-08-27 icu_beds 157
## 8 AK 2021-08-27 icu_beds_occupied 134.
## 9 AK 2021-08-27 adult_icu_covid 26.4
## 10 AK 2021-09-03 icu_beds 159.
## # … with 15,902 more rows
## # ℹ Use `print(n = ...)` to see more rowshospVars <- c("adult_beds_covid", "adult_beds", "adult_beds_occupied", "total_beds")
checkHospitalMetrics(indivHosp_20220805,
startDate="2020-07-31",
endDate="2022-07-24",
keyVars=hospVars,
plotTitle="Aug 2022 Hospital Data",
returnData=TRUE
)
## # A tibble: 21,216 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-08-07 total_beds 13
## 2 AK 2020-08-07 adult_beds 0
## 3 AK 2020-08-07 adult_beds_occupied 0
## 4 AK 2020-08-07 adult_beds_covid 0
## 5 AK 2020-09-11 total_beds 192
## 6 AK 2020-09-11 adult_beds 0
## 7 AK 2020-09-11 adult_beds_occupied 0
## 8 AK 2020-09-11 adult_beds_covid 0
## 9 AK 2021-08-27 total_beds 1248
## 10 AK 2021-08-27 adult_beds 1137.
## # … with 21,206 more rows
## # ℹ Use `print(n = ...)` to see more rowscheckHospitalMetrics(readFromRDS("indivHosp_20220704"),
startDate="2020-07-31",
endDate="2022-06-20",
keyVars=icuVars,
plotTitle="Jul 2022 ICU Data",
returnData=TRUE
)
## # A tibble: 15,147 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-07-31 icu_beds 216
## 2 AK 2020-07-31 icu_beds_occupied 92.9
## 3 AK 2020-07-31 adult_icu_covid 0
## 4 AK 2020-08-07 icu_beds 196.
## 5 AK 2020-08-07 icu_beds_occupied 91.4
## 6 AK 2020-08-07 adult_icu_covid 5.4
## 7 AK 2020-08-14 icu_beds 216
## 8 AK 2020-08-14 icu_beds_occupied 101.
## 9 AK 2020-08-14 adult_icu_covid 0
## 10 AK 2020-08-21 icu_beds 216.
## # … with 15,137 more rows
## # ℹ Use `print(n = ...)` to see more rowscheckHospitalMetrics(readFromRDS("indivHosp_20220704"),
startDate="2020-07-31",
endDate="2022-06-20",
keyVars=hospVars,
plotTitle="Jul 2022 Hospital Data",
returnData=TRUE
)
## # A tibble: 20,196 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-07-31 total_beds 1670.
## 2 AK 2020-07-31 adult_beds 356.
## 3 AK 2020-07-31 adult_beds_occupied 516.
## 4 AK 2020-07-31 adult_beds_covid 32.1
## 5 AK 2020-08-07 total_beds 1667.
## 6 AK 2020-08-07 adult_beds 356
## 7 AK 2020-08-07 adult_beds_occupied 534.
## 8 AK 2020-08-07 adult_beds_covid 38
## 9 AK 2020-08-14 total_beds 1659
## 10 AK 2020-08-14 adult_beds 356.
## # … with 20,186 more rows
## # ℹ Use `print(n = ...)` to see more rowsThere are significant discontinuities in the most recent hospital data, with a number of states also having discontinuities in previous data. Data are saved for comparing overall hospital statistics:
allVars <- c(hospVars, icuVars)
hosp_20220805 <- checkHospitalMetrics(indivHosp_20220805,
startDate="2020-07-31",
endDate="2022-07-24",
keyVars=allVars,
plotTitle="Aug 2022 Hospital Data",
createPlot=FALSE
)
hosp_20220704 <- checkHospitalMetrics(readFromRDS("indivHosp_20220704"),
startDate="2020-07-31",
endDate="2022-06-20",
keyVars=allVars,
plotTitle="Jul 2022 Hospital Data",
createPlot=FALSE
)
hosp_20220805## # A tibble: 37,128 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-08-07 total_beds 13
## 2 AK 2020-08-07 adult_beds 0
## 3 AK 2020-08-07 adult_beds_occupied 0
## 4 AK 2020-08-07 adult_beds_covid 0
## 5 AK 2020-08-07 icu_beds 0
## 6 AK 2020-08-07 icu_beds_occupied 0
## 7 AK 2020-08-07 adult_icu_covid 0
## 8 AK 2020-09-11 total_beds 192
## 9 AK 2020-09-11 adult_beds 0
## 10 AK 2020-09-11 adult_beds_occupied 0
## # … with 37,118 more rows
## # ℹ Use `print(n = ...)` to see more rowshosp_20220704## # A tibble: 35,343 × 4
## state collection_week name value
## <chr> <date> <chr> <dbl>
## 1 AK 2020-07-31 total_beds 1670.
## 2 AK 2020-07-31 adult_beds 356.
## 3 AK 2020-07-31 adult_beds_occupied 516.
## 4 AK 2020-07-31 adult_beds_covid 32.1
## 5 AK 2020-07-31 icu_beds 216
## 6 AK 2020-07-31 icu_beds_occupied 92.9
## 7 AK 2020-07-31 adult_icu_covid 0
## 8 AK 2020-08-07 total_beds 1667.
## 9 AK 2020-08-07 adult_beds 356
## 10 AK 2020-08-07 adult_beds_occupied 534.
## # … with 35,333 more rows
## # ℹ Use `print(n = ...)` to see more rowsData are combined, with key metrics plotted:
hosp_All <- hosp_20220704 %>%
rename(value_220704=value) %>%
full_join(hosp_20220805 %>% rename(value_220805=value), by=c("state", "collection_week", "name"))
hosp_All %>%
mutate(year=lubridate::year(collection_week)) %>%
group_by(year, name) %>%
summarize(across(.fns=function(x) sum(ifelse(is.na(x), 1, 0))), .groups="drop") %>%
print(n=21)## # A tibble: 21 × 6
## year name state collection_week value_220704 value_220805
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020 adult_beds 0 0 0 971
## 2 2020 adult_beds_covid 0 0 0 971
## 3 2020 adult_beds_occupied 0 0 0 971
## 4 2020 adult_icu_covid 0 0 0 971
## 5 2020 icu_beds 0 0 0 971
## 6 2020 icu_beds_occupied 0 0 0 971
## 7 2020 total_beds 0 0 0 971
## 8 2021 adult_beds 0 0 0 1711
## 9 2021 adult_beds_covid 0 0 0 1711
## 10 2021 adult_beds_occupied 0 0 0 1711
## 11 2021 adult_icu_covid 0 0 0 1711
## 12 2021 icu_beds 0 0 0 1711
## 13 2021 icu_beds_occupied 0 0 0 1711
## 14 2021 total_beds 0 0 0 1711
## 15 2022 adult_beds 0 0 255 0
## 16 2022 adult_beds_covid 0 0 255 0
## 17 2022 adult_beds_occupied 0 0 255 0
## 18 2022 adult_icu_covid 0 0 255 0
## 19 2022 icu_beds 0 0 255 0
## 20 2022 icu_beds_occupied 0 0 255 0
## 21 2022 total_beds 0 0 255 0# Comparison of raw data values
hosp_All %>%
filter(name=="total_beds") %>%
rename(metric=name) %>%
pivot_longer(-c(state, collection_week, metric)) %>%
ggplot(aes(x=collection_week)) +
geom_line(aes(y=ifelse(is.na(value), 0, value), group=name, color=name)) +
geom_point(data=~filter(., is.na(value)), aes(y=0, color=name)) +
facet_wrap(~state, scales="free_y") +
labs(title="Comparison by data source", subtitle="Metric: total_beds", y=NULL, x=NULL, caption="Points are NA")
# Percent difference
hosp_All %>%
filter(name=="total_beds") %>%
rename(metric=name) %>%
mutate(val1=value_220704,
val2=value_220805,
maxVal=pmax(ifelse(is.na(val1), 0, val1), val2, na.rm=TRUE),
diffVal=ifelse(is.na(val2), 0, val2)-ifelse(is.na(val1), 0, val1),
pct=ifelse(maxVal==0, 0, diffVal/maxVal)
) %>%
ggplot(aes(x=collection_week)) +
geom_line(aes(y=pct)) +
facet_wrap(~state) +
geom_hline(yintercept=0, lty=2) +
geom_vline(xintercept=as.Date(c("2021-08-27", "2022-06-24")), lty=2) +
labs(title="Comparison by data source",
subtitle="Metric: total_beds",
y="delta / max",
x=NULL,
caption="Dashed lines for disconnects\nat 2021-08-27 and 2022-06-24"
)
Plotting is converted to functional form:
plotDeltaHospital <- function(df,
keyMetric,
keyStates=c(state.abb, "DC"),
keyTimes=NULL,
aggStates=c()
) {
# FUNCTION ARGUMENTS:
# df: tibble or data frame with data formatted like hosp_All
# keyMetric: metric of interest for plotting
# keyStates: states of interest for plotting
# keyTimes: variables representing data by time period (NULL means all numeric)
# aggStates: named vector of states that should be aggregated (e.g., list("AZ"="Desert SW", "NV"="Desert SW"))
# Create keyTimes if not passed
if(is.null(keyTimes)) keyTimes <- df %>% select(where(is.numeric)) %>% names()
# Base data
df <- df %>%
filter(name %in% all_of(keyMetric), state %in% all_of(keyStates)) %>%
rename(metric=name) %>%
select(state, collection_week, metric, all_of(keyTimes))
# Aggregate if requested
if(length(aggStates) > 0) {
df <- df %>%
mutate(state=ifelse(state %in% names(aggStates), aggStates[state], state)) %>%
group_by(state, collection_week, metric) %>%
summarize(across(.fns=specNA(sum)), .groups="drop")
}
# Volume differences
p1 <- df %>%
pivot_longer(-c(state, collection_week, metric)) %>%
ggplot(aes(x=collection_week)) +
geom_line(aes(y=ifelse(is.na(value), 0, value), group=name, color=name)) +
geom_point(data=~filter(., is.na(value)), aes(y=0, color=name)) +
facet_wrap(~state, scales="free_y") +
labs(title="Comparison by data source",
subtitle=paste0("Metric: ", keyMetric),
y=NULL,
x=NULL,
caption="Points are NA"
)
print(p1)
# Renaming vector
vecRename <- c("val1", "val2") %>% purrr::set_names(c(min(keyTimes), max(keyTimes)))
# Percent differences
p2 <- df %>%
colRenamer(vecRename=vecRename) %>%
mutate(maxVal=pmax(ifelse(is.na(val1), 0, val1), val2, na.rm=TRUE),
diffVal=ifelse(is.na(val2), 0, val2)-ifelse(is.na(val1), 0, val1),
pct=ifelse(maxVal==0, 0, diffVal/maxVal)
) %>%
ggplot(aes(x=collection_week)) +
geom_line(aes(y=pct)) +
facet_wrap(~state) +
geom_hline(yintercept=0, lty=2) +
labs(title=paste0("Comparison by data source: ", min(names(vecRename)), " vs. ", max(names(vecRename))),
subtitle=paste0("Metric: ", keyMetric),
y="delta / max",
x=NULL
)
print(p2)
# geom_vline(xintercept=as.Date(c("2021-08-27", "2022-06-24")), lty=2) +
# caption="Dashed lines for disconnects\nat 2021-08-27 and 2022-06-24"
}
# Individual metrics for capacity
plotDeltaHospital(hosp_All, keyMetric="total_beds")
plotDeltaHospital(hosp_All, keyMetric="adult_beds")
plotDeltaHospital(hosp_All, keyMetric="icu_beds")
# Aggregated to US metrics for capacity
vecStates <- rep("US", 51) %>% purrr::set_names(c(state.abb, "DC"))
plotDeltaHospital(hosp_All, keyMetric="total_beds", aggStates=vecStates)
plotDeltaHospital(hosp_All, keyMetric="adult_beds", aggStates=vecStates)
plotDeltaHospital(hosp_All, keyMetric="icu_beds", aggStates=vecStates)
# Aggregated metrics for burden
plotDeltaHospital(hosp_All, keyMetric="adult_beds_occupied", aggStates=vecStates)
plotDeltaHospital(hosp_All, keyMetric="adult_beds_covid", aggStates=vecStates)
plotDeltaHospital(hosp_All, keyMetric="icu_beds_occupied", aggStates=vecStates)
plotDeltaHospital(hosp_All, keyMetric="adult_icu_covid", aggStates=vecStates)
ICU data seem consistent if the historical data are used to back-fill missing current data. Total beds and adult beds data appear to have a sharp fall-off in both data sources around 2022-01, in addition to missing historical entries in the latest data
The drop in ‘adult_beds’ is explored:
# Explore for state: NY
nyTest08 <- indivHosp_20220805 %>%
filter(state=="NY") %>%
mutate(keyMetric=ifelse(all_adult_hospital_beds_7_day_avg==-999999, NA, all_adult_hospital_beds_7_day_avg),
yyyyqq=paste0(lubridate::year(collection_week), "-Q", lubridate::quarter(collection_week))
)
nyTest08## # A tibble: 7,714 × 130
## hospital…¹ collecti…² state ccn hospi…³ address city zip hospi…⁴ fips_…⁵
## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 330279 2022-06-24 NY 3302… MERCY … 565 AB… BUFF… 14220 Short … 36029
## 2 330279 2022-05-27 NY 3302… MERCY … 565 AB… BUFF… 14220 Short … 36029
## 3 330405 2022-05-27 NY 3304… HELEN … 51 NOR… WEST… 10993 Short … 36087
## 4 330279 2022-01-21 NY 3302… MERCY … 565 AB… BUFF… 14220 Short … 36029
## 5 330163 2021-09-10 NY 3301… EASTER… 521 EA… LOCK… 14094 Short … 36063
## 6 330030 2020-09-25 NY 3300… NEWARK… 111 DR… NEWA… 14513 Short … 36117
## 7 330245 2020-09-18 NY 3302… ST ELI… 2209 G… UTICA 13501 Short … 36065
## 8 330030 2020-09-18 NY 3300… NEWARK… 111 DR… NEWA… 14513 Short … 36117
## 9 330224 2020-09-11 NY 3302… HEALTH… 105 MA… KING… 12401 Short … 36111
## 10 330397 2020-09-04 NY 3303… INTERF… 1545 A… BROO… 11213 Short … 36047
## # … with 7,704 more rows, 120 more variables: is_metro_micro <lgl>,
## # total_beds_7_day_avg <dbl>, all_adult_hospital_beds_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_beds_7_day_avg <dbl>,
## # inpatient_beds_used_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl>,
## # inpatient_beds_used_covid_7_day_avg <dbl>,
## # total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl>, …
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesnyTest08 %>%
group_by(hospital_pk, yyyyqq) %>%
summarize(nNA=mean(is.na(keyMetric)), keyMetric=mean(ifelse(is.na(keyMetric), 0, keyMetric)), .groups="drop") %>%
group_by(yyyyqq) %>%
summarize(sum=sum(keyMetric, na.rm=TRUE), pctNA=mean(nNA))## # A tibble: 6 × 3
## yyyyqq sum pctNA
## <chr> <dbl> <dbl>
## 1 2020-Q3 19 0.917
## 2 2021-Q3 48736. 0
## 3 2021-Q4 47674. 0.000433
## 4 2022-Q1 24858. 0.576
## 5 2022-Q2 19589. 0.694
## 6 2022-Q3 19377. 0.707nyTest07 <- readFromRDS("indivHosp_20220704") %>%
filter(state=="NY") %>%
mutate(keyMetric=ifelse(all_adult_hospital_beds_7_day_avg==-999999, NA, all_adult_hospital_beds_7_day_avg),
yyyyqq=paste0(lubridate::year(collection_week), "-Q", lubridate::quarter(collection_week))
)
nyTest07## # A tibble: 16,212 × 130
## hospital…¹ collecti…² state ccn hospi…³ address city zip hospi…⁴ fips_…⁵
## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 332006 2022-04-15 NY 3320… CALVAR… 1740 -… BRONX 10461 Long T… 36005
## 2 330405 2022-02-11 NY 3304… HELEN … 51 NOR… WEST… 10993 Short … 36087
## 3 330410 2020-10-09 NY 3304… TERENC… 1249 F… NEW … 10029 Short … 36061
## 4 331313 2020-10-09 NY 3313… SCHUYL… 220 ST… MONT… 14865 Critic… 36097
## 5 331309 2020-10-02 NY 3313… RIVER … 4 FULL… ALEX… 13607 Critic… 36045
## 6 330030 2020-09-25 NY 3300… NEWARK… 111 DR… NEWA… 14513 Short … 36117
## 7 330030 2020-09-18 NY 3300… NEWARK… 111 DR… NEWA… 14513 Short … 36117
## 8 330397 2020-09-04 NY 3303… INTERF… 1545 A… BROO… 11213 Short … 36047
## 9 330030 2020-09-04 NY 3300… NEWARK… 111 DR… NEWA… 14513 Short … 36117
## 10 330406 2020-08-14 NY 3304… SUNNYV… 1270 B… SCHE… 12308 Short … 36093
## # … with 16,202 more rows, 120 more variables: is_metro_micro <lgl>,
## # total_beds_7_day_avg <dbl>, all_adult_hospital_beds_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_beds_7_day_avg <dbl>,
## # inpatient_beds_used_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl>,
## # inpatient_beds_used_covid_7_day_avg <dbl>,
## # total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl>, …
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesnyTest07 %>%
group_by(hospital_pk, yyyyqq) %>%
summarize(nNA=mean(is.na(keyMetric)), keyMetric=mean(ifelse(is.na(keyMetric), 0, keyMetric)), .groups="drop") %>%
group_by(yyyyqq) %>%
summarize(sum=sum(keyMetric, na.rm=TRUE), pctNA=mean(nNA))## # A tibble: 8 × 3
## yyyyqq sum pctNA
## <chr> <dbl> <dbl>
## 1 2020-Q3 43631. 0.225
## 2 2020-Q4 52706. 0.0222
## 3 2021-Q1 52102. 0
## 4 2021-Q2 51956. 0
## 5 2021-Q3 49938. 0
## 6 2021-Q4 47674. 0.000430
## 7 2022-Q1 24858. 0.572
## 8 2022-Q2 19511. 0.690# Explore 2021-Q2 to 2022-Q2 decline by hospital
testSummary <- nyTest07 %>%
group_by(hospital_pk, yyyyqq) %>%
summarize(nNA=mean(is.na(keyMetric)), keyMetric=mean(ifelse(is.na(keyMetric), 0, keyMetric)), .groups="drop") %>%
filter(yyyyqq %in% c("2021-Q2", "2022-Q2")) %>%
mutate(name=ifelse(yyyyqq==min(yyyyqq), "older", "newer")) %>%
pivot_wider(hospital_pk, names_from="name", values_from="keyMetric", values_fill=0) %>%
mutate(delta=newer-older, pctDelta=delta/older) %>%
arrange(delta)
colSums(testSummary %>% select(older, newer, delta))## older newer delta
## 51955.96 19510.92 -32445.04testReport <- testSummary %>%
left_join(unique(select(nyTest07, hospital_pk, hospital_name, city, hospital_subtype)), by="hospital_pk")
testReport %>%
ggplot(aes(x=delta)) +
geom_histogram(bins=50, fill="lightblue") +
labs(x=NULL, y="# Hospitals", title="Change in reported adult_beds in NY hospitals Q2-2021 to Q2-2022")
testReport %>%
print(n=25)## # A tibble: 166 × 8
## hospital_pk older newer delta pctDelta hospital_name city hospi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 330024 1549. 0 -1549. -1 MOUNT SINAI HOSPITAL NEW … Short …
## 2 330059 1442 0 -1442 -1 MONTEFIORE MEDICAL CEN… BRONX Short …
## 3 330285 1188 0 -1188 -1 STRONG MEMORIAL HOSPIT… ROCH… Short …
## 4 330005 979 0 -979 -1 KALEIDA HEALTH BUFF… Short …
## 5 330046 903 0 -903 -1 MOUNT SINAI WEST NEW … Short …
## 6 330194 859 0 -859 -1 MAIMONIDES MEDICAL CEN… BROO… Short …
## 7 330125 856 0 -856 -1 ROCHESTER GENERAL HOSP… ROCH… Short …
## 8 330234 731 0 -731 -1 WESTCHESTER MEDICAL CE… VALH… Short …
## 9 330241 671 0 -671 -1 UNIVERSITY HOSPITAL S … SYRA… Short …
## 10 330393 669 0 -669 -1 STONY BROOK UNIVERSITY… STON… Short …
## 11 330219 648 0 -648 -1 ERIE COUNTY MEDICAL CE… BUFF… Short …
## 12 330013 647 0 -647 -1 ALBANY MEDICAL CENTER … ALBA… Short …
## 13 330154 605 0 -605 -1 MEMORIAL HOSPITAL FOR … NEW … Short …
## 14 330226 524 0 -524 -1 UNITY HOSPITAL OF ROCH… ROCH… Short …
## 15 330126 503 0 -503 -1 GARNET HEALTH MEDICAL … MIDD… Short …
## 16 330169 499. 0 -499. -1 MOUNT SINAI BETH ISRAEL NEW … Short …
## 17 330203 489 0 -489 -1 CROUSE HOSPITAL SYRA… Short …
## 18 330027 483 0 -483 -1 NASSAU UNIVERSITY MEDI… EAST… Short …
## 19 330009 478 3.83 -474. -0.992 BRONXCARE HOSPITAL CEN… BRONX Short …
## 20 330057 508 39.2 -469. -0.923 ST PETER'S HOSPITAL ALBA… Short …
## 21 330286 461 0 -461 -1 GOOD SAMARITAN HOSPITA… WEST… Short …
## 22 330394 443 0 -443 -1 UNITED HEALTH SERVICES… BING… Short …
## 23 330140 451 22.5 -428. -0.950 ST JOSEPH'S HOSPITAL H… SYRA… Short …
## 24 330164 426. 0 -426. -1 HIGHLAND HOSPITAL ROCH… Short …
## 25 330023 417. 0 -417. -1 VASSAR BROTHERS MEDICA… POUG… Short …
## # … with 141 more rows, and abbreviated variable name ¹hospital_subtype
## # ℹ Use `print(n = ...)` to see more rowstestReport %>%
tail(20)## # A tibble: 20 × 8
## hospital_pk older newer delta pctDelta hospital_name city hospi…¹
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 330350 216 209. -7.09 -0.0328 SUNY HEALTH SCIENCE … BROO… Short …
## 2 330166 13 5.92 -7.08 -0.544 WESTFIELD MEMORIAL H… WEST… Short …
## 3 331310 31 25 -6 -0.194 ELLENVILLE REGIONAL … ELLE… Critic…
## 4 331307 26 22.7 -3.31 -0.127 CLIFTON FINE HOSPITAL STAR… Critic…
## 5 330157 147. 144. -2.48 -0.0169 SAMARITAN MEDICAL CE… WATE… Short …
## 6 330065 124 123. -1.22 -0.00981 NIAGARA FALLS MEMORI… NIAG… Short …
## 7 330008 64 64 0 0 WYOMING COUNTY COMMU… WARS… Short …
## 8 330224 0 0 0 NaN HEALTHALLIANCE HOSPI… KING… Short …
## 9 330399 347 347 0 0 ST BARNABAS HOSPITAL BRONX Short …
## 10 330403 0 0 0 NaN MONROE COMMUNITY HOS… ROCH… Short …
## 11 331304 19 19 0 0 MARGARETVILLE MEMORI… MARG… Critic…
## 12 331318 32 32 0 0 CARTHAGE AREA HOSPIT… CART… Critic…
## 13 332008 201 201 0 0 HENRY J CARTER SPECI… NEW … Long T…
## 14 333301 0 0 0 NaN BLYTHEDALE CHILDREN'… VALH… Childr…
## 15 330182 449 452. 2.82 0.00629 ST FRANCIS HOSPITAL,… ROSL… Short …
## 16 330160 799. 804. 4.51 0.00565 STATEN ISLAND UNIVER… STAT… Short …
## 17 330261 216. 228 11.6 0.0537 PHELPS HOSPITAL SLEE… Short …
## 18 330162 218 238 20 0.0917 NORTHERN WESTCHESTER… MOUN… Short …
## 19 330043 381. 408. 27.1 0.0712 SOUTHSIDE HOSPITAL BAY … Short …
## 20 330119 495. 636 141. 0.284 LENOX HILL HOSPITAL NEW … Short …
## # … with abbreviated variable name ¹hospital_subtypeMany NY hospitals appear to have stopped reporting adult_beds data. The process is converted to functional form:
exploreReportedChange <- function(dfNew,
dfOld,
useMetric="all_adult_hospital_beds_7_day_avg",
naMetric=-999999,
lstFilter=list(),
lstExclude=list()
) {
# FUNCTION ARGUMENTS
# dfNew: the new data frame
# dfOld: the old data frame
# useMetric: metric to explore
# naMetric: vector of values to treat as NA
# lstFilter: a list for filtering records, of form list("field"=c("allowed values"))
# lstExclude: a list for filtering records, of form list("field"=c("disallowed values"))
# Helper function to create frames
helperFrameCreator <- function(df) {
df %>%
rowFilter(lstFilter=lstFilter, lstExclude=lstExclude) %>%
mutate(keyMetric=ifelse(get(useMetric)==-999999, NA, get(useMetric)),
customTime=paste0(lubridate::year(collection_week), "-Q", lubridate::quarter(collection_week))
)
}
# Create the data frames
testNew <- helperFrameCreator(dfNew)
testOld <- helperFrameCreator(dfOld)
# Helper function to summarize key metrics
helperGroupMetrics <- function(df, lab=NULL) {
if(!is.null(lab)) cat("\nData summary for: ", lab, "\n")
df %>%
group_by(hospital_pk, customTime) %>%
summarize(nNA=mean(is.na(keyMetric)),
keyMetric=mean(ifelse(is.na(keyMetric), 0, keyMetric)),
.groups="drop"
) %>%
group_by(customTime) %>%
summarize(sum=sum(keyMetric, na.rm=TRUE), pctNA=mean(nNA))
}
helperGroupMetrics(testNew) %>%
full_join(helperGroupMetrics(testOld), by=c("customTime"), suffix=c("_newer", "_older")) %>%
select(customTime, starts_with("sum"), starts_with("pct"), everything()) %>%
arrange(customTime) %>%
print()
}
exploreReportedChange(dfNew=indivHosp_20220805,
dfOld=readFromRDS("indivHosp_20220704"),
lstFilter=list("state"=c("NY"))
)## # A tibble: 9 × 5
## customTime sum_newer sum_older pctNA_newer pctNA_older
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-Q3 19 43631. 0.917 0.225
## 2 2020-Q4 NA 52706. NA 0.0222
## 3 2021-Q1 NA 52102. NA 0
## 4 2021-Q2 NA 51956. NA 0
## 5 2021-Q3 48736. 49938. 0 0
## 6 2021-Q4 47674. 47674. 0.000433 0.000430
## 7 2022-Q1 24858. 24858. 0.576 0.572
## 8 2022-Q2 19589. 19511. 0.694 0.690
## 9 2022-Q3 19377. NA 0.707 NA